Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study
BackgroundPrivate-part skin diseases (PPSDs) can cause a patient’s stigma, which may hinder the early diagnosis of these diseases. Artificial intelligence (AI) is an effective tool to improve the early diagnosis of PPSDs, especially in preventing the deterioration of skin tum...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2024-12-01
|
| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2024/1/e52914 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850059623992655872 |
|---|---|
| author | Wei Wang Xiang Chen Licong Xu Kai Huang Shuang Zhao Yong Wang |
| author_facet | Wei Wang Xiang Chen Licong Xu Kai Huang Shuang Zhao Yong Wang |
| author_sort | Wei Wang |
| collection | DOAJ |
| description |
BackgroundPrivate-part skin diseases (PPSDs) can cause a patient’s stigma, which may hinder the early diagnosis of these diseases. Artificial intelligence (AI) is an effective tool to improve the early diagnosis of PPSDs, especially in preventing the deterioration of skin tumors in private parts such as Paget disease. However, to our knowledge, there is currently no research on using AI to identify PPSDs due to the complex backgrounds of the lesion areas and the challenges in data collection.
ObjectiveThis study aimed to develop and evaluate an AI-aided diagnosis system for the detection and classification of PPSDs: aiding patients in self-screening and supporting dermatologists’ diagnostic enhancement.
MethodsIn this decision analytical modeling study, a 2-stage AI-aided diagnosis system was developed to classify PPSDs. In the first stage, a multitask detection network was trained to automatically detect and classify skin lesions (type, color, and shape). In the second stage, we proposed a knowledge graph based on dermatology expertise and constructed a decision network to classify seven PPSDs (condyloma acuminatum, Paget disease, eczema, pearly penile papules, genital herpes, syphilis, and Bowen disease). A reader study with 13 dermatologists of different experience levels was conducted. Dermatologists were asked to classify the testing cohort under reading room conditions, first without and then with system support. This AI-aided diagnostic study used the data of 635 patients from two institutes between July 2019 and April 2022. The data of Institute 1 contained 2701 skin lesion samples from 520 patients, which were used for the training of the multitask detection network in the first stage. In addition, the data of Institute 2 consisted of 115 clinical images and the corresponding medical records, which were used for the test of the whole 2-stage AI-aided diagnosis system.
ResultsOn the test data of Institute 2, the proposed system achieved the average precision, recall, and F1-score of 0.81, 0.86, and 0.83, respectively, better than existing advanced algorithms. For the reader performance test, our system improved the average F1-score of the junior, intermediate, and senior dermatologists by 16%, 7%, and 4%, respectively.
ConclusionsIn this study, we constructed the first skin-lesion–based dataset and developed the first AI-aided diagnosis system for PPSDs. This system provides the final diagnosis result by simulating the diagnostic process of dermatologists. Compared with existing advanced algorithms, this system is more accurate in identifying PPSDs. Overall, our system can not only help patients achieve self-screening and alleviate their stigma but also assist dermatologists in diagnosing PPSDs. |
| format | Article |
| id | doaj-art-fe344ec8a5294c119c18de8cc2be58fe |
| institution | DOAJ |
| issn | 1438-8871 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-fe344ec8a5294c119c18de8cc2be58fe2025-08-20T02:50:51ZengJMIR PublicationsJournal of Medical Internet Research1438-88712024-12-0126e5291410.2196/52914Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling StudyWei Wanghttps://orcid.org/0000-0003-3984-6241Xiang Chenhttps://orcid.org/0000-0001-8187-636XLicong Xuhttps://orcid.org/0000-0002-0207-4754Kai Huanghttps://orcid.org/0000-0003-3348-2723Shuang Zhaohttps://orcid.org/0000-0001-7081-6177Yong Wanghttps://orcid.org/0000-0001-7670-3958 BackgroundPrivate-part skin diseases (PPSDs) can cause a patient’s stigma, which may hinder the early diagnosis of these diseases. Artificial intelligence (AI) is an effective tool to improve the early diagnosis of PPSDs, especially in preventing the deterioration of skin tumors in private parts such as Paget disease. However, to our knowledge, there is currently no research on using AI to identify PPSDs due to the complex backgrounds of the lesion areas and the challenges in data collection. ObjectiveThis study aimed to develop and evaluate an AI-aided diagnosis system for the detection and classification of PPSDs: aiding patients in self-screening and supporting dermatologists’ diagnostic enhancement. MethodsIn this decision analytical modeling study, a 2-stage AI-aided diagnosis system was developed to classify PPSDs. In the first stage, a multitask detection network was trained to automatically detect and classify skin lesions (type, color, and shape). In the second stage, we proposed a knowledge graph based on dermatology expertise and constructed a decision network to classify seven PPSDs (condyloma acuminatum, Paget disease, eczema, pearly penile papules, genital herpes, syphilis, and Bowen disease). A reader study with 13 dermatologists of different experience levels was conducted. Dermatologists were asked to classify the testing cohort under reading room conditions, first without and then with system support. This AI-aided diagnostic study used the data of 635 patients from two institutes between July 2019 and April 2022. The data of Institute 1 contained 2701 skin lesion samples from 520 patients, which were used for the training of the multitask detection network in the first stage. In addition, the data of Institute 2 consisted of 115 clinical images and the corresponding medical records, which were used for the test of the whole 2-stage AI-aided diagnosis system. ResultsOn the test data of Institute 2, the proposed system achieved the average precision, recall, and F1-score of 0.81, 0.86, and 0.83, respectively, better than existing advanced algorithms. For the reader performance test, our system improved the average F1-score of the junior, intermediate, and senior dermatologists by 16%, 7%, and 4%, respectively. ConclusionsIn this study, we constructed the first skin-lesion–based dataset and developed the first AI-aided diagnosis system for PPSDs. This system provides the final diagnosis result by simulating the diagnostic process of dermatologists. Compared with existing advanced algorithms, this system is more accurate in identifying PPSDs. Overall, our system can not only help patients achieve self-screening and alleviate their stigma but also assist dermatologists in diagnosing PPSDs.https://www.jmir.org/2024/1/e52914 |
| spellingShingle | Wei Wang Xiang Chen Licong Xu Kai Huang Shuang Zhao Yong Wang Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study Journal of Medical Internet Research |
| title | Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study |
| title_full | Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study |
| title_fullStr | Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study |
| title_full_unstemmed | Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study |
| title_short | Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study |
| title_sort | artificial intelligence aided diagnosis system for the detection and classification of private part skin diseases decision analytical modeling study |
| url | https://www.jmir.org/2024/1/e52914 |
| work_keys_str_mv | AT weiwang artificialintelligenceaideddiagnosissystemforthedetectionandclassificationofprivatepartskindiseasesdecisionanalyticalmodelingstudy AT xiangchen artificialintelligenceaideddiagnosissystemforthedetectionandclassificationofprivatepartskindiseasesdecisionanalyticalmodelingstudy AT licongxu artificialintelligenceaideddiagnosissystemforthedetectionandclassificationofprivatepartskindiseasesdecisionanalyticalmodelingstudy AT kaihuang artificialintelligenceaideddiagnosissystemforthedetectionandclassificationofprivatepartskindiseasesdecisionanalyticalmodelingstudy AT shuangzhao artificialintelligenceaideddiagnosissystemforthedetectionandclassificationofprivatepartskindiseasesdecisionanalyticalmodelingstudy AT yongwang artificialintelligenceaideddiagnosissystemforthedetectionandclassificationofprivatepartskindiseasesdecisionanalyticalmodelingstudy |