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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Deep skin diseases diagnostic system with Dual-channel Image and Extracted Text.

Huanyu Li^1,2, Peng Zhang³, Zikun Wei²

¹The Third Affiliated Hospital of Chongqing Medical University (CQMU), Chongqing, China.

Frontiers in Artificial Intelligence

|November 6, 2023

Summary

AI models offer reliable skin disease diagnosis. Our DIET-AI model, using dual-channel images and text from over 200,000 Asian records, matches senior doctors’ diagnostic accuracy for common skin conditions.

Keywords:

artificial intelligence computer vision dermatitis digital medicine skin disease

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Area of Science:

Artificial Intelligence in Medicine
Dermatology
Medical Imaging Analysis

Background:

Skin disease diagnosis is challenging due to laboratory test unreliability.
Existing AI diagnostic models lack integration of images and text, are scarce for Asian populations, and cover limited common diseases.

Purpose of the Study:

To develop and evaluate a deep learning-based AI system (DIET-AI) for diagnosing common skin diseases.
To assess the diagnostic performance of DIET-AI using both images and medical records from an Asian cohort.

Main Methods:

Developed DIET-AI, a dual-channel deep learning model, utilizing over 200,000 images and 220,000 medical records from China.
Prospectively collected data from 6,043 cases across 15 hospitals.
Compared DIET-AI's diagnostic performance against six physicians of varying seniorities.

Main Results:

DIET-AI demonstrated average performance comparable to or exceeding that of physicians across 31 common skin diseases.
Analysis of area under the curve, sensitivity, and specificity confirmed DIET-AI's clinical effectiveness.
Medical records were found to influence the performance of both DIET-AI and physicians.

Conclusions:

This study presents the largest dermatological dataset for the Chinese demographic to date.
DIET-AI, a novel dual-channel model using images and medical records, achieved diagnostic performance comparable to senior dermatologists for common non-cancerous skin conditions.
The findings support the feasibility and performance evaluation of DIET-AI for clinical application.