Letter to the Editor Open Access
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Mar 28, 2024; 16(3): 69-71
Published online Mar 28, 2024. doi: 10.4329/wjr.v16.i3.69
Artificial intelligence for disease diagnostics still has a long way to go
Jian-She Yang, Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
Jian-She Yang, Qiang Wang, Basic Medicine College, Gansu Medical College, Pingliang 744000, Gansu Province, China
Zhong-Wei Lv, Department of Nuclear Medicine, Shanghai Tenth People’s Hospital of Nanjing Medical University, Shanghai 200072, China
ORCID number: Jian-She Yang (0000-0001-7069-6072); Qiang Wang (0000-0002-9855-6730); Zhong-Wei Lv (0000-0003-3370-5560).
Author contributions: Yang JS, Wang Q, and Lv ZW designed the research, analyzed the data and wrote the paper.
Supported by the Dean Responsible Project of Gansu Medical College, No. GY-2023FZZ01; University Teachers Innovation Fund Project of Gansu Province, No. 2023A-182; and Key Research Project of Pingliang Science and Technology, No. PL-STK-2021A-004.
Conflict-of-interest statement: All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jian-She Yang, MD, MSc, PhD, Academic Editor, Academic Fellow, Chairman, Chief Technician, Dean, Full Professor, Shanghai Tenth People's Hospital, Tongji University School of Medicine, No. 301 Yanchang Road (M), Shanghai 200072, China. 2305499@tongji.edu.cn
Received: January 4, 2024
Peer-review started: January 4, 2024
First decision: March 2, 2024
Revised: March 6, 2024
Accepted: March 14, 2024
Article in press: March 14, 2024
Published online: March 28, 2024
Processing time: 81 Days and 23.1 Hours

Abstract

Artificial intelligence (AI) can sometimes resolve difficulties that other advanced technologies and humans cannot. In medical diagnostics, AI has the advantage of processing figure recognition, especially for images with similar characteristics that are difficult to distinguish with the naked eye. However, the mechanisms of this advanced technique should be well-addressed to elucidate clinical issues. In this letter, regarding an original study presented by Takayama et al, we suggest that the authors should effectively illustrate the mechanism and detailed procedure that artificial intelligence techniques processing the acquired images, including the recognition of non-obvious difference between the normal parts and pathological ones, which were impossible to be distinguished by naked eyes, such as the basic constitutional elements of pixels and grayscale, special molecules or even some metal ions which involved into the diseases occurrence.

Key Words: Artificial intelligence; Figure recognition; Diagnosis; AI interactive mechanisms

Core Tip: We strengthened the importance of mechanism elucidation of the advanced artificial intelligence in processing figures recognition, especially for those images with very similar characteristics that are difficult to be distinguished by the naked eye, and expressed a caution on decision making by using artificial intelligence technique for medical use, in that the unidentified potential would result in a bias.



TO THE EDITOR

Recently, Takayama et al[1] reported that a branch of artificial intelligence (AI), namely, deep learning (DL), combined with reduced-field-of-view (reduced-FOV) diffusion-weighted imaging, which was identified as field-of-view optimized and constrained undistorted single-shot, has greatly improved image quality without prolonging the scan time for pancreatic cystic lesion diagnostics.

This is an very interested work related the current hot-topic, while, due to the technical shortages, further investigation need to be done during the near future. In terms of these issues, the authors haven’t outlined and addressed it in this work rationally. Here we presented some of shortcomings.

In this work, authors have applied the artificial intelligence to distinguish the images for identified diagnosis of pancreatic disease from other related or concurrent diseases, they should also analyze all types of pancreatic images by this technique as systematically as possible. Given the variety of diseases, even the physiological status of pancreatic disease can present diverse physical and chemical characteristics, which are the bases on which AI operates. However, by simply applying the commercial AIR™ Recon DL algorithm (GE Healthcare), the authors have not provided readers the essential and enough information which mentioned above, even in the form of a supplementary material. A complete work should describe the phenomenon with its potential mechanism. Though the AI basic procedures and regulations have been well established by scientists, this interactive episode was absent in this study.

AI can sometimes resolve difficulties that other advanced technologies and humans cannot[2,3]. The authors should effectively illustrate the mechanism and detailed procedure that artificial intelligence techniques processing the acquired images, including the recognition of non-obvious difference between the normal parts and pathological ones of pancreatic, which were not sensitive to naked eyes, such as the pixels and grayscale, special molecules or even some metal ions which involved into the diseases occurrence. All of these presentation will facilitate the understanding of AI processing and recognizing similar or confused images. These are the fundamental principles for artificial intelligence applying in medical use.

Footnotes

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Computer science, artificial intelligence

Country/Territory of origin: China

Peer-review report’s scientific quality classification

Grade A (Excellent): 0

Grade B (Very good): B

Grade C (Good): 0

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: Alsamhi SH, Ireland S-Editor: Liu JH L-Editor: A P-Editor: Zhao S

References
1.  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;15:338-349.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
2.  Sahu HK, Kumar S, Alsamhi SH, Chaube MK, Curry E.   Novel Framework for Alzheimer Early Diagnosis using Inductive Transfer Learning Techniques. Proceedings of the 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA); 2022 Oct 25-26; Ibb, Yemen. United States: IEEE, 2022: 1-7.  [PubMed]  [DOI]  [Cited in This Article: ]
3.  Kumar S, Chaube MK, Alsamhi SH, Gupta SK, Guizani M, Gravina R, Fortino G. A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques. Comput Methods Programs Biomed. 2022;226:107109.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 7]  [Cited by in F6Publishing: 4]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]