00068967

January 04, 2024, 08:00

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We have read the article by Takayama, et al,[1] who described that a branch of artificial intelligence (AI), deep learning (DL), combined with reduced-field-of-view (reduced-FOV) diffusion-weighted imaging (DWI) which was identified as FOCUS has greatly improved image quality without prolonging the scan time for pancreatic cystic lesions diagnostics. This topic is of great interest, and needs to be further investigated for a long period of time in the future. However, the authors have not outlined and described it in a rational way. The obvious shortcomings of this work are described as follows: Given that the authors aimed to discuss the application of AI in pancreatic diseases imaging, they should have depicted all types of pancreatic images as systematically as possible, in that the various diseases, even the physiological status of pancreatic will present diverse characteristics in terms of their physical and chemical nature, which are the bases that AI works on. However, the authors have failed to provide readers with enough systematical information, and with a holistic vision of AI working, by just applying a commercial algorithm AIR™ Recon DL (GE Healthcare). An excellent work should not only describe the phenomena alone, but it should also discuss the potential mechanism. The common mechanisms of AI seem to be well-known, but there is a lack of description for interactive episode in this work. AI, sometimes, can resolve the difficulties that other advanced technologies or human beings could not do. Thus, in this work, the authors should have made great efforts to describe how AI processes images. Whether AI can recognize the diversity of the graphic grayscale, special molecules, or even some metal ions, and how it works? How does AI distinguish the disease from the surrounding normal tissues? All of these principles and advancements should be clarified as detailed as possible. Additionally, although the authors wanted to describe and summarize the advances and advantages of AI, they failed to provide more information systematically. Reference Takayama Y, Sato K, Tanaka S, Murayama R, Goto N, Yoshimitsu K. Deep learning-based magnetic resonance imaging reconstruction for improving the image quality of reduced-field-of-view diffusion-weighted imaging of the pancreas. World J Radiol 2023; 15(12): 338-349 [DOI: 10.4329/wjr.v15.i12.338]

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