Liu SW, Li P, Li XQ, Wang Q, Duan JY, Chen J, Li RH, Guo YF. Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders. World J Gastroenterol 2025; 31(23): 105076 [PMID: 40575335 DOI: 10.3748/wjg.v31.i23.105076]
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08628847
Submitted on:
June 21, 2025, 08:04
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Reader Comments:
This paper provides a timely and insightful review on the application of machine learning (ML) in diagnosing and treating esophageal disorders, including conditions like GERD, esophageal cancer, and achalasia. The study highlights the potential of AI in enhancing diagnostic accuracy and personalizing treatment strategies, which could greatly improve patient outcomes in gastroenterology. The comprehensive synthesis of current literature and the focus on ML’s role in automating diagnosis and optimizing treatment are highly relevant for clinical practice. The manuscript offers significant value by showing how ML can transform the management of esophageal diseases and improve patient care.
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Reader's ID:
03721258
Submitted on:
June 20, 2025, 12:14
Reader Expertise:
Reader’s expertise on the topic of the manuscript
Conflicts-of-Interest Statement:
Does the reader have a conflict of interest?
Reader Comment Standards for Published Articles:
1 Title
Does the title reflect the main subject/hypothesis of the manuscript?
2 Abstract
Does the abstract summarize and reflect the work described in the manuscript?
3 Key Words
Do the key words reflect the focus of the manuscript?
4 Background
Does the manuscript adequately describe the background, present status and significance of the study?
5 Methods
Does the manuscript describe methods (e.g., experiments, data analysis, surveys, and clinical trials, etc.) in adequate detail?
6 Results
Are the research objectives achieved by the experiments used in this study?
Has the study made meaningful contributions towards research progress in this field?
7 Discussion
Does the manuscript interpret the findings adequately and appropriately, highlighting the key points concisely, clearly and logically?
Are the findings and their applicability/relevance to the literature stated in a clear and definite manner?
Is the Discussion accurate and does it discuss the paper’s scientific significance and/or relevance to clinical practice sufficiently?
8 Illustrations and Tables
Are the figures, diagrams and tables sufficient, good quality and appropriately illustrative of the paper contents?
Do figures require labeling with arrows, asterisks, etc., or better legends?
9 Biostatistics
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10 Units
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11 References
Does the manuscript appropriately cite the latest, important and authoritative references in the Introduction and Discussion sections?
Does the author self-cite, omit, incorrectly cite and/or over-cite references?
12 Quality of manuscript organization and presentation
Is the manuscript concisely and coherently organized and presented?
Are the style, language and grammar accurate and appropriate?
13 Ethics statements
For all manuscripts involving human studies and/or animal experiments, author(s) must submit the related formal ethics documents that were reviewed and approved by their local ethical review committee. Did the manuscript meet the requirements of ethics?
Scientific Quality:
The overall quality of the manuscript, based on the above-listed criteria, should be evaluated and classified according to the following five categories
Language Quality:
Language quality (style, grammar, and spelling) should be evaluated and classified according to the following five categories.
Reader Comments:
This review synthesizes machine learning (ML) applications in esophageal disorders, emphasizing three critical advances: (1) Automated analysis of multimodal diagnostic data achieving accuracy rates of 80%-95% across different conditions; (2) Integration of deep learning with endoscopic imaging enabling real-time assistance in diagnosis and risk stratification; and (3) Development of novel non-invasive screening approaches through ML-based biomarker identification. The convergence of artificial intelligence with clinical medicine demonstrates transformative potential in addressing current diagnostic challenges and enabling precision medicine in esophageal disease management.
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