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1
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Peppercorn J, Blayney DW, Bosserman L, Cox J. Glory Days: Celebrating Two Decades of Advances in Cancer Care on the 20th Anniversary of JCO Oncology Practice. JCO Oncol Pract 2025; 21:581-586. [PMID: 40359629 DOI: 10.1200/op-25-00273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2025] [Accepted: 03/24/2025] [Indexed: 05/15/2025] Open
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Mascarenhas M, Martins M, Ribeiro T, Afonso J, Cardoso P, Mendes F, Cardoso H, Almeida R, Ferreira J, Fonseca J, Macedo G. Software as a Medical Device (SaMD) in Digestive Healthcare: Regulatory Challenges and Ethical Implications. Diagnostics (Basel) 2024; 14:2100. [PMID: 39335779 PMCID: PMC11431531 DOI: 10.3390/diagnostics14182100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Revised: 08/29/2024] [Accepted: 09/05/2024] [Indexed: 09/30/2024] Open
Abstract
The growing integration of software in healthcare, particularly the rise of standalone software as a medical device (SaMD), is transforming digestive medicine, a field heavily reliant on medical imaging for both diagnosis and therapeutic interventions. This narrative review aims to explore the impact of SaMD on digestive healthcare, focusing on the evolution of these tools and their regulatory and ethical challenges. Our analysis highlights the exponential growth of SaMD in digestive healthcare, driven by the need for precise diagnostic tools and personalized treatment strategies. This rapid advancement, however, necessitates the parallel development of a robust regulatory framework to ensure SaMDs are transparent and deliver universal clinical benefits without the introduction of bias or harm. In addition, the discussion highlights the importance of adherence to the FAIR principles for data management-findability, accessibility, interoperability, and reusability. However, enhanced accessibility and interoperability require rigorous protocols to ensure compliance with data protection guidelines and adequate data security, both of which are crucial for effective integration of SaMDs into clinical workflows. In conclusion, while SaMDs hold significant promise for improving patients' outcomes in digestive medicine, their successful integration into clinical workflow depends on rigorous data protection protocols and clinical validation. Future directions include the need for adequate clinical and real-world studies to demonstrate that these devices are safe and well-suited to healthcare settings.
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Affiliation(s)
- Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
| | - Hélder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Rute Almeida
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - João Ferreira
- Department of Mechanic Engineering, Faculty of Engineering of University of Porto, 4200 427 Porto, Portugal
- DigestAID-Digestive Artificial Intelligence Development, 4200 427 Porto, Portugal
| | - João Fonseca
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
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Wang Y, Huang C, Chang W, Lu W, Hui Q, Jiang S, Ouyang X, Kong D. UDRSNet: An unsupervised deformable registration module based on image structure similarity. Med Phys 2024; 51:4811-4826. [PMID: 38353628 DOI: 10.1002/mp.16986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 01/03/2024] [Accepted: 01/28/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Image registration is a challenging problem in many clinical tasks, but deep learning has made significant progress in this area over the past few years. Real-time and robust registration has been made possible by supervised transformation estimation. However, the quality of registrations using this framework depends on the quality of ground truth labels such as displacement field. PURPOSE To propose a simple and reliable method for registering medical images based on image structure similarity in a completely unsupervised manner. METHODS We proposed a deep cascade unsupervised deformable registration approach to align images without reliable clinical data labels. Our basic network was composed of a displacement estimation module (ResUnet) and a deformation module (spatial transformer layers). We adoptedl 2 $l_2$ -norm to regularize the deformation field instead of the traditionall 1 $l_1$ -norm regularization. Additionally, we utilized structural similarity (ssim) estimation during the training stage to enhance the structural consistency between the deformed images and the reference images. RESULTS Experiments results indicated that by incorporating ssim loss, our cascaded methods not only achieved higher dice score of 0.9873, ssim score of 0.9559, normalized cross-correlation (NCC) score of 0.9950, and lower relative sum of squared difference (SSD) error of 0.0313 on CT images, but also outperformed the comparative methods on ultrasound dataset. The statistical t $t$ -test results also proved that these improvements of our method have statistical significance. CONCLUSIONS In this study, the promising results based on diverse evaluation metrics have demonstrated that our model is simple and effective in deformable image registration (DIR). The generalization ability of the model was also verified through experiments on liver CT images and cardiac ultrasound images.
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Affiliation(s)
- Yun Wang
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Chongfei Huang
- China Mobile (Hangzhou) Information Technology Co., Ltd., Hangzhou, China
| | - Wanru Chang
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Wenliang Lu
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Qinglei Hui
- School of Mathematics and Statistics, Anyang Normal University, Anyang, China
| | - Siyuan Jiang
- Zhejiang Demetics Medical Technology Co., Ltd, Hangzhou, China
| | - Xiaoping Ouyang
- State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou, China
| | - Dexing Kong
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
- Zhejiang Qiushi Institute for Mathematical Medicine, Hangzhou, China
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Kamalanathan A, Muthu B, Kuniyil Kaleena P. Artificial Intelligence (AI) Game Changer in Cancer Biology. MARVELS OF ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE IN LIFE SCIENCES 2023:62-87. [DOI: 10.2174/9789815136807123010009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
Abstract
Healthcare is one of many industries where the most modern technologies,
such as artificial intelligence and machine learning, have shown a wide range of
applications. Cancer, one of the most prevalent non-communicable diseases in modern
times, accounts for a sizable portion of worldwide mortality. Investigations are
continuously being conducted to find ways to reduce cancer mortality and morbidity.
Artificial Intelligence (AI) is currently being used in cancer research, with promising
results. Two main features play a vital role in improving cancer prognosis: early
detection and proper diagnosis using imaging and molecular techniques. AI's use as a
tool in these sectors has demonstrated its capacity to precisely detect and diagnose,
which is one of AI's many applications in cancer research. The purpose of this chapter
is to review the literature and find AI applications in a range of cancers that are
commonly seen.
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Affiliation(s)
- Ashok Kamalanathan
- Department of Microbiology and Biotechnology, Faculty of Arts and Science, Bharath Institute
of Higher Education and Research (BIHER), Chennai- 600 073, Tamil Nadu, India
| | - Babu Muthu
- Department of Microbiology and Biotechnology, Faculty of Arts and Science, Bharath Institute
of Higher Education and Research (BIHER), Chennai- 600 073, Tamil Nadu, India
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Hathaway QA, Hogg JP, Lakhani DA. Need for Medical Student Education in Emerging Technologies and Artificial Intelligence: Fostering Enthusiasm, Rather Than Flight, From Specialties Most Affected by Emerging Technologies. Acad Radiol 2023; 30:1770-1771. [PMID: 36464546 DOI: 10.1016/j.acra.2022.11.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 12/03/2022]
Affiliation(s)
- Quincy A Hathaway
- School of Medicine, West Virginia University, 1 Medical Center Drive, Morgantown, WV, USA
| | - Jeffery P Hogg
- School of Medicine, West Virginia University, 1 Medical Center Drive, Morgantown, WV, USA; Department of Radiology, West Virginia University, 1 Medical Center Drive, Morgantown, WV, USA
| | - Dhairya A Lakhani
- Department of Radiology, West Virginia University, 1 Medical Center Drive, Morgantown, WV, USA.
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Yang X, Wu J, Chen X. Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma. J Clin Med 2023; 12:jcm12093077. [PMID: 37176518 PMCID: PMC10178972 DOI: 10.3390/jcm12093077] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023] Open
Abstract
Artificial intelligence (AI) is an interdisciplinary field that encompasses a wide range of computer science disciplines, including image recognition, machine learning, human-computer interaction, robotics and so on. Recently, AI, especially deep learning algorithms, has shown excellent performance in the field of image recognition, being able to automatically perform quantitative evaluation of complex medical image features to improve diagnostic accuracy and efficiency. AI has a wider and deeper application in the medical field of diagnosis, treatment and prognosis. Nasopharyngeal carcinoma (NPC) occurs frequently in southern China and Southeast Asian countries and is the most common head and neck cancer in the region. Detecting and treating NPC early is crucial for a good prognosis. This paper describes the basic concepts of AI, including traditional machine learning and deep learning algorithms, and their clinical applications of detecting and assessing NPC lesions, facilitating treatment and predicting prognosis. The main limitations of current AI technologies are briefly described, including interpretability issues, privacy and security and the need for large amounts of annotated data. Finally, we discuss the remaining challenges and the promising future of using AI to diagnose and treat NPC.
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Affiliation(s)
- Xinggang Yang
- Division of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
| | - Juan Wu
- Out-Patient Department, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
| | - Xiyang Chen
- Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
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7
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Tenajas R, Miraut D, Illana CI, Alonso-Gonzalez R, Arias-Valcayo F, Herraiz JL. Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning. APPLIED SCIENCES 2023; 13:3693. [DOI: 10.3390/app13063693] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/09/2024]
Abstract
Ultrasound (US) is a flexible imaging modality used globally as a first-line medical exam procedure in many different clinical cases. It benefits from the continued evolution of ultrasonic technologies and a well-established US-based digital health system. Nevertheless, its diagnostic performance still presents challenges due to the inherent characteristics of US imaging, such as manual operation and significant operator dependence. Artificial intelligence (AI) has proven to recognize complicated scan patterns and provide quantitative assessments for imaging data. Therefore, AI technology has the potential to help physicians get more accurate and repeatable outcomes in the US. In this article, we review the recent advances in AI-assisted US scanning. We have identified the main areas where AI is being used to facilitate US scanning, such as standard plane recognition and organ identification, the extraction of standard clinical planes from 3D US volumes, and the scanning guidance of US acquisitions performed by humans or robots. In general, the lack of standardization and reference datasets in this field makes it difficult to perform comparative studies among the different proposed methods. More open-access repositories of large US datasets with detailed information about the acquisition are needed to facilitate the development of this very active research field, which is expected to have a very positive impact on US imaging.
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Affiliation(s)
- Rebeca Tenajas
- Family Medicine Department, Centro de Salud de Arroyomolinos, Arroyomolinos, 28939 Madrid, Spain
| | - David Miraut
- Advanced Health Technology Department, GMV, Tres Cantos, 28760 Madrid, Spain
| | - Carlos I. Illana
- Advanced Health Technology Department, GMV, Tres Cantos, 28760 Madrid, Spain
| | | | - Fernando Arias-Valcayo
- Nuclear Physics Group and IPARCOS, Faculty of Physical Sciences, University Complutense of Madrid, CEI Moncloa, 28040 Madrid, Spain
| | - Joaquin L. Herraiz
- Nuclear Physics Group and IPARCOS, Faculty of Physical Sciences, University Complutense of Madrid, CEI Moncloa, 28040 Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain
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Double-Camera Fusion System for Animal-Position Awareness in Farming Pens. Foods 2022; 12:foods12010084. [PMID: 36613301 PMCID: PMC9818956 DOI: 10.3390/foods12010084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/06/2022] [Accepted: 12/09/2022] [Indexed: 12/29/2022] Open
Abstract
In livestock breeding, continuous and objective monitoring of animals is manually unfeasible due to the large scale of breeding and expensive labour. Computer vision technology can generate accurate and real-time individual animal or animal group information from video surveillance. However, the frequent occlusion between animals and changes in appearance features caused by varying lighting conditions makes single-camera systems less attractive. We propose a double-camera system and image registration algorithms to spatially fuse the information from different viewpoints to solve these issues. This paper presents a deformable learning-based registration framework, where the input image pairs are initially linearly pre-registered. Then, an unsupervised convolutional neural network is employed to fit the mapping from one view to another, using a large number of unlabelled samples for training. The learned parameters are then used in a semi-supervised network and fine-tuned with a small number of manually annotated landmarks. The actual pixel displacement error is introduced as a complement to an image similarity measure. The performance of the proposed fine-tuned method is evaluated on real farming datasets and demonstrates significant improvement in lowering the registration errors than commonly used feature-based and intensity-based methods. This approach also reduces the registration time of an unseen image pair to less than 0.5 s. The proposed method provides a high-quality reference processing step for improving subsequent tasks such as multi-object tracking and behaviour recognition of animals for further analysis.
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Hopkins DF, Visser RC, Armes J. Going paper-lite: housebound patient perspectives on the introduction of mobile working. Br J Community Nurs 2022; 27:508-514. [PMID: 36194397 DOI: 10.12968/bjcn.2022.27.10.508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Healthcare policies promote technology use as a means to modernise healthcare and support seamless, person-centred care. However, despite information technology (IT) use being common practice in clinical settings, its use in patients' homes is still developing. This study explored patients' perspectives on the use of IT and electronic health records (EHR) in their home environment. Semi structured interviews were conducted with housebound patients who received regular care from the district nursing team, and thematic data analysis was undertaken. Participants reported variable knowledge and experiences with mobile working and EHR. Most were positive and identified clear benefits for clinicians. However, few participants reported benefits to themselves. Contrary to popular belief, IT use is expected by older patients and, while barriers were identified, the overall opinion was positive. A digital divide was apparent, with some at risk of being disadvantaged by the increasing use of technology.
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Affiliation(s)
| | | | - Jo Armes
- Professor of Cancer Care, University of Surrey, Guildford, UK
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10
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Cao LL, Peng M, Xie X, Chen GQ, Huang SY, Wang JY, Jiang F, Cui XW, Dietrich CF. Artificial intelligence in liver ultrasound. World J Gastroenterol 2022; 28:3398-3409. [PMID: 36158262 PMCID: PMC9346461 DOI: 10.3748/wjg.v28.i27.3398] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/18/2022] [Accepted: 06/19/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is playing an increasingly important role in medicine, especially in the field of medical imaging. It can be used to diagnose diseases and predict certain statuses and possible events that may happen. Recently, more and more studies have confirmed the value of AI based on ultrasound in the evaluation of diffuse liver diseases and focal liver lesions. It can assess the severity of liver fibrosis and nonalcoholic fatty liver, differentially diagnose benign and malignant liver lesions, distinguish primary from secondary liver cancers, predict the curative effect of liver cancer treatment and recurrence after treatment, and predict microvascular invasion in hepatocellular carcinoma. The findings from these studies have great clinical application potential in the near future. The purpose of this review is to comprehensively introduce the current status and future perspectives of AI in liver ultrasound.
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Affiliation(s)
- Liu-Liu Cao
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Mei Peng
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Xiang Xie
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi 445000, Hubei Province, China
| | - Shu-Yan Huang
- Department of Medical Ultrasound, The First People's Hospital of Huaihua, Huaihua 418000, Hunan Province, China
| | - Jia-Yu Wang
- Department of Medical Ultrasound, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Fan Jiang
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern 3626, Switzerland
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Tamai Y, Iwasa M, Yoshida Y, Nomoto J, Kato T, Asuke H, Eguchi A, Takei Y, Nakagawa H. Development of a New Index to Distinguish Hepatic Encephalopathy through Automated Quantification of Globus Pallidal Signal Intensity Using MRI. Diagnostics (Basel) 2022; 12:diagnostics12071584. [PMID: 35885492 PMCID: PMC9317893 DOI: 10.3390/diagnostics12071584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 06/14/2022] [Accepted: 06/28/2022] [Indexed: 11/29/2022] Open
Abstract
Hyperintensities within the bilateral globus pallidus on T1-weighted magnetic resonance images were present in some liver cirrhosis patients with hepatic encephalopathy. The symptoms of covert hepatic encephalopathy are similar to those of mild dementia. We aimed to develop a new diagnostic index in which to distinguish hepatic encephalopathy from dementia. The globus pallidus signal hyperintensity was quantified using three-dimensional images. In addition, the new index value distribution was evaluated in a cohort of dementia patients. Signal intensity of globus pallidus significantly increased in liver cirrhosis patients with hepatic encephalopathy compared to those without hepatic encephalopathy (p < 0.05), healthy subjects (p < 0.05) or dementia patients (p < 0.001). Only 12.5% of liver cirrhosis patients without hepatic encephalopathy and 2% of dementia patients exceeded the new index cut-off value of 0.994, which predicts hepatic encephalopathy. One dementia patient in our evaluation had a history of liver cancer treatment and was assumed to have concomitant hepatic encephalopathy. The automatic assessment of signal intensity in globus pallidus is useful for distinguishing liver cirrhosis patients with hepatic encephalopathy from healthy subjects and liver cirrhosis patients without hepatic encephalopathy. Our image analyses exclude possible cases of hepatic encephalopathy from patients with neurocognitive impairment, including dementia.
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Affiliation(s)
- Yasuyuki Tamai
- Department of Gastroenterology and Hepatology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan; (Y.T.); (M.I.); (Y.T.); (H.N.)
| | - Motoh Iwasa
- Department of Gastroenterology and Hepatology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan; (Y.T.); (M.I.); (Y.T.); (H.N.)
| | - Yuichi Yoshida
- Department of Gastroenterology and Hepatology, Suita Municipal Hospital, Osaka 564-8567, Japan;
| | - Jun Nomoto
- HAMATO Neurosurgery Clinic, Yokohama 236-0052, Japan;
| | - Takahiro Kato
- Soubudai Neurosurgical Clinic, Sagamihara 252-0324, Japan;
| | - Hiroe Asuke
- Medical Affairs Department, ASKA Pharmaceutical Co., Ltd., Tokyo 108-8532, Japan;
| | - Akiko Eguchi
- Department of Gastroenterology and Hepatology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan; (Y.T.); (M.I.); (Y.T.); (H.N.)
- Correspondence: ; Fax: +81-59-231-5223
| | - Yoshiyuki Takei
- Department of Gastroenterology and Hepatology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan; (Y.T.); (M.I.); (Y.T.); (H.N.)
| | - Hayato Nakagawa
- Department of Gastroenterology and Hepatology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan; (Y.T.); (M.I.); (Y.T.); (H.N.)
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Taha A, Ochs V, Kayhan LN, Enodien B, Frey DM, Krähenbühl L, Taha-Mehlitz S. Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58040459. [PMID: 35454298 PMCID: PMC9029673 DOI: 10.3390/medicina58040459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/14/2022] [Accepted: 03/18/2022] [Indexed: 02/06/2023]
Abstract
Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.
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Affiliation(s)
- Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, 4123 Allschwil, Switzerland
- Correspondence:
| | - Vincent Ochs
- Roche Innovation Center Basel, Department of Pharma Research & Early Development, 4070 Basel, Switzerland;
| | - Leos N. Kayhan
- Department of Surgery, Canntonal Hospital Luzern, 6004 Luzern, Switzerland;
| | - Bassey Enodien
- Department of Surgery, Wetzikon Hospital, 8620 Wetzikon, Switzerland; (B.E.); (D.M.F.)
| | - Daniel M. Frey
- Department of Surgery, Wetzikon Hospital, 8620 Wetzikon, Switzerland; (B.E.); (D.M.F.)
| | | | - Stephanie Taha-Mehlitz
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, 4002 Basel, Switzerland;
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Abbasi S, Tavakoli M, Boveiri HR, Mosleh Shirazi MA, Khayami R, Khorasani H, Javidan R, Mehdizadeh A. Medical image registration using unsupervised deep neural network: A scoping literature review. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103444] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Wang S, Niu K, Chen L, Rao X. Method for counting labeled neurons in mouse brain regions based on image representation and registration. Med Biol Eng Comput 2022; 60:487-500. [PMID: 35015271 DOI: 10.1007/s11517-021-02495-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 12/18/2021] [Indexed: 11/25/2022]
Abstract
An important step in brain image analysis is to divide specific brain regions by matching brain slices to standard brain reference atlases, and perform statistical analysis on the labeled neurons in each brain region. Taking mouse fluorescently labeled brain slices as an example, due to the noise and distortion introduced during the preparation of brain slices, and the modal differences with standard brain atlas, the brain slices cannot directly establish an accurate one-to-one correspondence with the brain atlas, which in turn affects the accuracy of the number of labeled neurons in each brain region. This paper introduces the idea of image representation, uses neural networks to realize the registration of different modal mouse brain slices and brain atlas, completes the regional localization of the brain slices, and uses threshold segmentation to detect and count the labeled neurons in each brain region. The method proposed in this paper can effectively solve the problem of large deviation of neurons count caused by the inaccurate division of brain regions in large deformed brain slices, and can automatically realize accurate count of labeled neurons in each brain region of brain slices. The whole framework of method for counting labeled neurons in mouse brain regions based on image representation and registration.
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Affiliation(s)
- Songwei Wang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Ke Niu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Liwei Chen
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China.
| | - Xiaoping Rao
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Center for Magnetic Resonance, Innovation Academy for Precision Measurement Science and Methodology, Chinese Academy of Sciences, Wuhan, 430071, China.
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15
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Teufel A, Binder H. Clinical Decision Support Systems. Visc Med 2021; 37:491-498. [PMID: 35087899 PMCID: PMC8738909 DOI: 10.1159/000519420] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 09/03/2021] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND By combining up-to-date medical knowledge and steadily increasing patient data, a new level of medical care can emerge. SUMMARY AND KEY MESSAGES Clinical decision support systems (CDSSs) are an arising solution to handling rich data and providing them to health care providers in order to improve diagnosis and treatment. However, despite promising examples in many areas, substantial evidence for a thorough benefit of these support solutions is lacking. This may be due to a lack of general frameworks and diverse health systems around the globe. We therefore summarize the current status of CDSSs in medicine but also discuss potential limitations that need to be overcome in order to further foster future development and acceptance.
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Affiliation(s)
- Andreas Teufel
- Department of Medicine II, Section of Hepatology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health Baden-Württemberg (CPDBW), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Harald Binder
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
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16
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Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27:1664-1690. [PMID: 33967550 PMCID: PMC8072192 DOI: 10.3748/wjg.v27.i16.1664] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/11/2021] [Accepted: 03/17/2021] [Indexed: 02/06/2023] Open
Abstract
Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has achieved a breakthrough and revolutionized the processing methods of clinical medicine with the increasing workloads of medical records and digital images. Doctors are paying attention to AI technologies for various diseases in the fields of gastroenterology and hepatology. This review will illustrate AI technology procedures for medical image analysis, including data processing, model establishment, and model validation. Furthermore, we will summarize AI applications in endoscopy, radiology, and pathology, such as detecting and evaluating lesions, facilitating treatment, and predicting treatment response and prognosis with excellent model performance. The current challenges for AI in clinical application include potential inherent bias in retrospective studies that requires larger samples for validation, ethics and legal concerns, and the incomprehensibility of the output results. Therefore, doctors and researchers should cooperate to address the current challenges and carry out further investigations to develop more accurate AI tools for improved clinical applications.
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Affiliation(s)
- Jia-Sheng Cao
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Zi-Yi Lu
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Ming-Yu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Bin Zhang
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Sarun Juengpanich
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Jia-Hao Hu
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Shi-Jie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Win Topatana
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xue-Yin Zhou
- School of Medicine, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
| | - Xu Feng
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Ji-Liang Shen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Yu Liu
- College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xiu-Jun Cai
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
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17
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Cao SE, Zhang LQ, Kuang SC, Shi WQ, Hu B, Xie SD, Chen YN, Liu H, Chen SM, Jiang T, Ye M, Zhang HX, Wang J. Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography. World J Gastroenterol 2020; 26:3660-3672. [PMID: 32742134 PMCID: PMC7366064 DOI: 10.3748/wjg.v26.i25.3660] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 05/08/2020] [Accepted: 06/03/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The accurate classification of focal liver lesions (FLLs) is essential to properly guide treatment options and predict prognosis. Dynamic contrast-enhanced computed tomography (DCE-CT) is still the cornerstone in the exact classification of FLLs due to its noninvasive nature, high scanning speed, and high-density resolution. Since their recent development, convolutional neural network-based deep learning techniques has been recognized to have high potential for image recognition tasks.
AIM To develop and evaluate an automated multiphase convolutional dense network (MP-CDN) to classify FLLs on multiphase CT.
METHODS A total of 517 FLLs scanned on a 320-detector CT scanner using a four-phase DCE-CT imaging protocol (including precontrast phase, arterial phase, portal venous phase, and delayed phase) from 2012 to 2017 were retrospectively enrolled. FLLs were classified into four categories: Category A, hepatocellular carcinoma (HCC); category B, liver metastases; category C, benign non-inflammatory FLLs including hemangiomas, focal nodular hyperplasias and adenomas; and category D, hepatic abscesses. Each category was split into a training set and test set in an approximate 8:2 ratio. An MP-CDN classifier with a sequential input of the four-phase CT images was developed to automatically classify FLLs. The classification performance of the model was evaluated on the test set; the accuracy and specificity were calculated from the confusion matrix, and the area under the receiver operating characteristic curve (AUC) was calculated from the SoftMax probability outputted from the last layer of the MP-CDN.
RESULTS A total of 410 FLLs were used for training and 107 FLLs were used for testing. The mean classification accuracy of the test set was 81.3% (87/107). The accuracy/specificity of distinguishing each category from the others were 0.916/0.964, 0.925/0.905, 0.860/0.918, and 0.925/0.963 for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. The AUC (95% confidence interval) for differentiating each category from the others was 0.92 (0.837-0.992), 0.99 (0.967-1.00), 0.88 (0.795-0.955) and 0.96 (0.914-0.996) for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively.
CONCLUSION MP-CDN accurately classified FLLs detected on four-phase CT as HCC, metastases, benign non-inflammatory FLLs and hepatic abscesses and may assist radiologists in identifying the different types of FLLs.
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Affiliation(s)
- Su-E Cao
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, Guangdong Province, China
| | - Lin-Qi Zhang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, Guangdong Province, China
| | - Si-Chi Kuang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, Guangdong Province, China
| | - Wen-Qi Shi
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, Guangdong Province, China
| | - Bing Hu
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, Guangdong Province, China
| | - Si-Dong Xie
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, Guangdong Province, China
| | - Yi-Nan Chen
- Department of Scientific and Technological Research, 12 Sigma Technologies, Beijing 100102, China
| | - Hui Liu
- Department of Scientific and Technological Research, 12 Sigma Technologies, Beijing 100102, China
| | - Si-Min Chen
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, Guangdong Province, China
| | - Ting Jiang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, Guangdong Province, China
| | - Meng Ye
- Department of Scientific and Technological Research, 12 Sigma Technologies, Beijing 100102, China
| | - Han-Xi Zhang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, Guangdong Province, China
| | - Jin Wang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, Guangdong Province, China
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18
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Garcia-Cazares R, Merlos-Benitez M, Marquez-Romero JM. Role of the physical examination in the determination of etiology of ischemic stroke. Neurol India 2020; 68:282-287. [PMID: 32415006 DOI: 10.4103/0028-3886.284386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The actual investigation of the body of a patient by the clinician in search for the signs of the disease beginning with the primary vital signs and continues with the careful and attentive observation of the patient. This article reviews the key findings in the physical examination of patients with ischemic stroke that have the potential to indicate the etiology of the infarct and to help to choose the use of ancillary tests. Through a systematic search of articles published in English related to the physical examination of patients with stroke, we identified key findings in the vital signs and classic components of the physical exam (appearance of the patient, auscultation, and eye examination) that have shown clinical significance when determining ischemic stroke etiology. We further suggest that the prompt identification of such findings can translate into better use of diagnostic tools and selection of ancillary confirmatory tests, thus, reducing the time to etiology based treatment and secondary prevention of ischemic stroke. in this manuscript, we aim to show that even though nowadays the clinical skills tend to be overlooked due to the overreliance on technology, the physical exam continues to be a valuable tool in the clinician armamentarium when facing the challenge of a patient with ischemic stroke.
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19
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Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, Wu XL, Cui XW, Dietrich CF. Artificial intelligence in medical imaging of the liver. World J Gastroenterol 2019; 25:672-682. [PMID: 30783371 PMCID: PMC6378542 DOI: 10.3748/wjg.v25.i6.672] [Citation(s) in RCA: 132] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2018] [Revised: 12/24/2018] [Accepted: 01/09/2019] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians' workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.
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Affiliation(s)
- Li-Qiang Zhou
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Jia-Yu Wang
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Song-Yuan Yu
- Department of Ultrasound, Tianyou Hospital Affiliated to Wuhan University of Technology, Wuhan 430030, Hubei Province, China
| | - Ge-Ge Wu
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Qi Wei
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - You-Bin Deng
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Xing-Long Wu
- School of Mathematics and Computer Science, Wuhan Textitle University, Wuhan 430200, Hubei Province, China
| | - Xin-Wu Cui
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Christoph F Dietrich
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Würzburg, Würzburg 97980, Germany
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20
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Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.
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Affiliation(s)
- Ahmed Hosny
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA
- Department of Radiology, New York Presbyterian Hospital, New York, NY, USA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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21
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Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.
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Affiliation(s)
- Ahmed Hosny
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA
- Department of Radiology, New York Presbyterian Hospital, New York, NY, USA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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22
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Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.
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Affiliation(s)
- Ahmed Hosny
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA
- Department of Radiology, New York Presbyterian Hospital, New York, NY, USA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Whippen D, Deering MJ, Ambinder EP. Advancing high-quality cancer care: cancer biomedical informatics grid supports personalized medicine and the electronic health record. J Oncol Pract 2011; 3:208-11. [PMID: 20859412 DOI: 10.1200/jop.0743501] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Kanas G, Morimoto L, Mowat F, O’Malley C, Fryzek J, Nordyke R. Use of electronic medical records in oncology outcomes research. CLINICOECONOMICS AND OUTCOMES RESEARCH 2010; 2:1-14. [PMID: 21935310 PMCID: PMC3169956 DOI: 10.2147/ceor.s8411] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2010] [Indexed: 11/23/2022] Open
Abstract
Oncology outcomes research could benefit from the use of an oncology-specific electronic medical record (EMR) network. The benefits and challenges of using EMR in general health research have been investigated; however, the utility of EMR for oncology outcomes research has not been explored. Compared to current available oncology databases and registries, an oncology-specific EMR could provide comprehensive and accurate information on clinical diagnoses, personal and medical histories, planned and actual treatment regimens, and post-treatment outcomes, to address research questions from patients, policy makers, the pharmaceutical industry, and clinicians/researchers. Specific challenges related to structural (eg, interoperability, data format/entry), clinical (eg, maintenance and continuity of records, variety of coding schemes), and research-related (eg, missing data, generalizability, privacy) issues must be addressed when building an oncology-specific EMR system. Researchers should engage with medical professional groups to guide development of EMR systems that would ultimately help improve the quality of cancer care through oncology outcomes research.
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25
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Cox JV. The Voice of ASCO. J Oncol Pract 2006. [DOI: 10.1200/jop.2006.2.1.36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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