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

Abstract

BackgroundDue 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.MethodsLeveraging 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.ResultsThe 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.ConclusionThis 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.

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

“…In contrast, Kaur and Kaur [52] focused on feature fusion using multiple networks, which is a different strategy of combining strengths of various architectures. Li, et al [53] created DIET-AI, which combined dual-channel images and extracted text for diagnosing 31 common skin diseases. This system was based on a large dataset from the Asian population and demonstrated diagnostic performance comparable to senior doctors.…”

“…Bala, Hossain, Hossain, Abdullah, Rahman, Manavalan, Gu, Islam and Huang [34] created a new dataset (MSID) for monkeypox, underlining the importance of dataset development in emerging medical conditions. Part of studies focused on the developed models that integrated diverse data types [53,90]. To elaborate, Li, Zhang, Wei, Qian, Tang, Hu, Huang, Xia, Zhang, Cheng, Yu, Zhang, Dan, Liu, Ye, He, Jiang, Liu, Fan, Song, Zhou, Wang, Zhang and Lv [53] combined images and medical records to diagnose skin diseases, while El Saleh, Chantaf and Nait-ali [90] focused on facial skin diseases, using images captured under various conditions.…”

“…Part of studies focused on the developed models that integrated diverse data types [53,90]. To elaborate, Li, Zhang, Wei, Qian, Tang, Hu, Huang, Xia, Zhang, Cheng, Yu, Zhang, Dan, Liu, Ye, He, Jiang, Liu, Fan, Song, Zhou, Wang, Zhang and Lv [53] combined images and medical records to diagnose skin diseases, while El Saleh, Chantaf and Nait-ali [90] focused on facial skin diseases, using images captured under various conditions. Bozkurt [91] emphasized data augmentation to address dataset limitations.…”

Advancements in acne detection: application of the CenterNet network in smart dermatology

“…In previous experiments, it demonstrated a high recognition rate for acne (75.98%) through extensive testing on 33 different facial skin diseases, demonstrating its accuracy in skin disease recognition. Therefore, this article on the basis of more in-depth acne type identification ( 23 ). Our study developed an acne detection model that harnesses the power of the CenterNet network, realized through a collaborative effort between dermatologists and computer science researchers.…”

Hybrid cosmetic dermatology: AI generated horizon

“…There are several studies conducted and published that confirm the reliability of AI diagnostic systems in dermatology; as an instance, DIET‐AI. 4 , 5 , 6 The risks in cosmetic dermatological practice start with the unrealistic expectations within the pre‐treatment period. Untailored treatment plan is another risk that augments the probability of post‐treatment complications.…”