A deep learning, image based approach for automated diagnosis for inflammatory skin diseases

Haijing Wu, Heng Yin, Haipeng Chen, Moyuan Sun, Xiaoqing Liu, Yizhou Yu, Yang Tang, Hai Long et al.

Annals of Translational Medicine 2020 article cited by 126

Source Open-access PDF More in Medicine & Health

importance 5/10 ai assisted Cutaneous Melanoma Detection and Management dermatology stage: research

Problem

Inflammatory skin diseases such as psoriasis, eczema and atopic dermatitis are easily misdiagnosed in clinical practice.

Why it matters

Misdiagnosis of these common skin diseases leads to wrong or delayed treatment for patients.

What the AI did

AIDDA, a CNN-based diagnosis assistant built on EfficientNet-b4, classifies clinical images into healthy skin, psoriasis, and eczema/atopic dermatitis.

Results

Overall accuracy 95.80%±0.09%, sensitivity 94.40%±0.12%, specificity 97.20%±0.06% on expert-confirmed test images.

Impact

Evidence supports strong internal test performance; no clinical deployment or external validation stated.

Record

Venue	Annals of Translational Medicine
Published	2020
Work type	article
Authors	Haijing Wu, Heng Yin, Haipeng Chen, Moyuan Sun, Xiaoqing Liu, Yizhou Yu, Yang Tang, Hai Long et al.
Citations	126 (OpenAlex, at import time)
References	23
Model or method	AIDDA (EfficientNet-b4 CNN)
Organization	not stated in source
Primary source	https://doi.org/10.21037/atm.2020.04.39
Open access	diamond
Keywords	skin disease, CNN, EfficientNet, diagnosis assistant, psoriasis, eczema

Importance rating

5 / 10

real but narrower advance

Misdiagnosis of common inflammatory skin diseases harms patients, but abstract quantifies only internal test accuracy; no prevalence, deployment, or external validation stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

More in Medicine & Health

#	Paper	Year	Citations	Importance
1	Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction Yeong Chan Lee, Jiho Cha, Injeong Shim, Woong‐Yang Park, Se Woong Kang...	2023	107	10
2	Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization Tawsifur Rahman, Amith Abdullah Khandakar, Muhammad Abdul Kadir, Khand...	2020	651	10
3	A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach Nitish Biswas, Khandaker Mohammad Mohi Uddin, Sarreha Tasmin Rikta, Sa...	2022	116	10
4	Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning Hamada R. H. Al-Absi, Mohammad Tariqul Islam, Mahmoud Ahmed Refaee, Mu...	2022	89	10
5	Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential Kruthi Suvarna, Deeptarup Biswas, Medha Gayathri J. Pai, Arup Acharjee...	2021	76	10