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Li H, Zhang P, Wei Z, Qian T, Tang Y, Hu K, Huang X, Xia X, Zhang Y, Cheng H, Yu F, Zhang W, Dan K, Liu X, Ye S, He G, Jiang X, Liu L, Fan Y, Song T, Zhou G, Wang Z, Zhang D, Lv J. Deep skin diseases diagnostic system with Dual-channel Image and Extracted Text. Front Artif Intell 2023; 6:1213620. [PMID: 37928449 PMCID: PMC10620802 DOI: 10.3389/frai.2023.1213620] [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: 04/28/2023] [Accepted: 09/12/2023] [Indexed: 11/07/2023] Open
Abstract
Background Due to the lower reliability of laboratory tests, skin diseases are more suitable for diagnosis with AI models. There are limited AI dermatology diagnostic models combining images and text; few of these are for Asian populations, and few cover the most common types of diseases. Methods Leveraging a dataset sourced from Asia comprising over 200,000 images and 220,000 medical records, we explored a deep learning-based system for Dual-channel images and extracted text for the diagnosis of skin diseases model DIET-AI to diagnose 31 skin diseases, which covers the majority of common skin diseases. From 1 September to 1 December 2021, we prospectively collected images from 6,043 cases and medical records from 15 hospitals in seven provinces in China. Then the performance of DIET-AI was compared with that of six doctors of different seniorities in the clinical dataset. Results The average performance of DIET-AI in 31 diseases was not less than that of all the doctors of different seniorities. By comparing the area under the curve, sensitivity, and specificity, we demonstrate that the DIET-AI model is effective in clinical scenarios. In addition, medical records affect the performance of DIET-AI and physicians to varying degrees. Conclusion This is the largest dermatological dataset for the Chinese demographic. For the first time, we built a Dual-channel image classification model on a non-cancer dermatitis dataset with both images and medical records and achieved comparable diagnostic performance to senior doctors about common skin diseases. It provides references for exploring the feasibility and performance evaluation of DIET-AI in clinical use afterward.
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Affiliation(s)
- Huanyu Li
- The Third Affiliated Hospital of Chongqing Medical University (CQMU), Chongqing, China
- Shanghai Botanee Bio-technology AI Lab, Shanghai, China
| | - Peng Zhang
- School of Medicine, Shanghai University, Shanghai, China
| | - Zikun Wei
- Shanghai Botanee Bio-technology AI Lab, Shanghai, China
| | - Tian Qian
- The Third Affiliated Hospital of Chongqing Medical University (CQMU), Chongqing, China
| | - Yiqi Tang
- Shanghai Botanee Bio-technology AI Lab, Shanghai, China
| | - Kun Hu
- Shanghai Botanee Bio-technology AI Lab, Shanghai, China
| | - Xianqiong Huang
- Department of Dermatology, Army Medical Center, Chongqing, China
| | - Xinxin Xia
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Yishuang Zhang
- School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Haixing Cheng
- The Third Affiliated Hospital of Chongqing Medical University (CQMU), Chongqing, China
| | - Fubing Yu
- The Third Affiliated Hospital of Chongqing Medical University (CQMU), Chongqing, China
| | - Wenjia Zhang
- Shanghai Botanee Bio-technology AI Lab, Shanghai, China
| | - Kena Dan
- The Third Affiliated Hospital of Chongqing Medical University (CQMU), Chongqing, China
| | - Xuan Liu
- Faculty of Science, The University of Sydney, Sydney, NSW, Australia
| | - Shujun Ye
- Faculty of Science, The University of Melbourne, Parkville, VIC, Australia
| | - Guangqiao He
- The Third Affiliated Hospital of Chongqing Medical University (CQMU), Chongqing, China
| | - Xia Jiang
- The Third Affiliated Hospital of Chongqing Medical University (CQMU), Chongqing, China
| | - Liwei Liu
- Chongqing Shapingba District People's Hospital, Chongqing, China
| | - Yukun Fan
- The Third Affiliated Hospital of Chongqing Medical University (CQMU), Chongqing, China
| | - Tingting Song
- The Third Affiliated Hospital of Chongqing Medical University (CQMU), Chongqing, China
| | - Guomin Zhou
- Shanghai Medical College, Fudan University, Shanghai, China
| | - Ziyi Wang
- Huazhong Agricultural University, Wuhan, Hubei, China
| | - Daojun Zhang
- The Third Affiliated Hospital of Chongqing Medical University (CQMU), Chongqing, China
| | - Junwei Lv
- Shanghai Botanee Bio-technology AI Lab, Shanghai, China
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