A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance: Simplifying the diagnosis of complicated diseases

Wu, Haijing; Yin, Heng; Chen, Haipeng; Sun, Moyuan; Liu, Xiaoqing; Yu, Yao; Tang, Yang; Long, Hai; Zhang, Bo; Zhou, Ying; Li, Yaping; Zhang, Guiyuing; Zhang, Peng; Zhan, Yi; Liao, Jingyi; Luo, Shuaihantian; Xiao, Rong; Su, Yuwen; Zhao, Juanjuan; Wang, Fei; Zhang, Jing; Zhang, Wei; Zhang, Jin; Hu, Kai; Yuan, Limei; Deng, Di; Liang, Yunsheng; Yang, Bin; Lu, Qianjin

doi:10.1016/j.jaad.2021.02.043

Cited by 16 publications

(16 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the number of clinical skin images and multi-IHC images are quite di cult to balance because the number of skin lesions is generally less than the number of views you can capture under the microscope. In a previous study that included 9241 clinical images, the diagnostic accuracies of SLE, SCLE, and DLE were 65.9%, 54.6% and 72.9%, respectively 27 . Although we did not include a plethora of clinical images, our conclusion of clinical skin image-based single-modal imaging obtained similar results, whereas the MMDLS performed much better.…”

Section: Discussionmentioning

confidence: 95%

See 1 more Smart Citation

A Multimodal Deep Learning-based Platform that Assists in the Classification of Lupus Subtypes and Differential Diagnosis Among Lupus and Other Similar Skin Diseases

Yang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Lupus erythematosus (LE) has the reputation of being the Great Imitator due to its various cutaneous manifestations, which often lead to difficulties in accurate diagnosis and classification. Even with the assistance of deep learning systems (DLSs), clinical skin image-based single modalities still experience difficulty distinguishing subtypes of LE. Here, we present an application of multimodal DLS (MMDLS), in which the training database consists of clinical skin images, multi-immunohistochemistry (multi-IHC) images and the 2019 European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) scores, to assist in the classification of LE subtypes (discoid LE (DLE), annular subacute cutaneous LE (A-SCLE), papulosquamous subacute cutaneous LE (P-SCLE), systemic LE (SLE)) and to aid in the differential diagnosis of other similar skin diseases. A total of 386 cases with 580 clinical skin images, 3380 multi-IHC images and 2019 EULAR/ACR scores from 25 institutions in China were included, and EfficientNet-B3 and ResNet-18 were utilized in this study. By comparing the single-modal and dual-modal DLS performances in multiple classifications, we demonstrate the superiority of the MMDLS (Ave-Sen = 0.9116, Ave-Spe = 0.9921, Ave-Pre = 0.9281). Moreover, the performance of MMDLS on 13 classifications was superior to that of dermatologists and pathologists (F1 score: dermatologists = 0.4471, pathologists = 0.6691, MMDLS = 0.9582) with an average area under the curve (AUC) of 0.9956. These results highlight the potential of the combined application of MMDLS and multi-IHC images to assist pathologists and dermatologists in diagnosing LE subtypes and similar skin diseases.

show abstract

Section: Discussionmentioning

confidence: 95%

“…Our results showed that the clustering result constantly improved with the fusion of multimodal information. However, although we made progress compared to previous studies 21,27 , the differentiation of A-SCLE and P-SCLE remains challenging.…”

Section: Classi Cation E Ciency Of 13 Categories By Proposed Mmdlsmentioning

confidence: 94%

A Multimodal Deep Learning-based Platform that Assists in the Classification of Lupus Subtypes and Differential Diagnosis Among Lupus and Other Similar Skin Diseases

Yang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…AI provides a wide road to explore and hence is currently being in adoption to every discipline of study [7]. AI, along with its two-subfield machine learning (ML) and Deep Learning (DL), is now vastly used in pharmaceutical sectors for pharmacophore modeling, molecular designing, pharmacological data analysis, assay analysis, chemical synthesis, and drug trial monitoring [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review on the application of artificial intelligence in drug discovery.

Sahoo

Dar

2021

TABCJ

View full text Add to dashboard Cite

The 21st century is witnessing immense achievements in human history, starting from home science to space science. Artificial Intelligence (AI) is a salient one among these feats, the critical factor of the 4th industrial revolution. Health is the primary and essential asset for the continuity of human civilization on this planet. Not only must we address the deadly existing diseases like Cancer, AIDS, Alzheimer's, heart diseases, gastrointestinal diseases, etc., but on top of that, we must effectively predict, prevent and respond to potential pathogens capable of causing havoc like the recent outbreak caused by SARS-CoV-2. AI-enabled technology with the computational capacity of a computer and reasoning ability of humans saves surplus labor and time that is majorly consumed in target validation, lead optimization, molecular representation, and designing reaction pathways, which traditionally is a decade-long way of searching, visualizing, studying, imagining, experimenting and maintaining a ton of data. This article would focus on how AI will help find the drug-like properties in the compound screening phase predicting the Structure-Activity Relationship (SAR) and ADMET properties in lead identification and optimization phases, sustainable development of chemicals in the synthesis phases up to AI's assistance in the successful conduct of clinical trials and repurposing.

show abstract

“…To the Editor: We read with interest the work of Wu et al 1 in applying a convolutional neural network to the diagnosis of cutaneous lupus erythematosus (CLE). We discuss the issues of the transparency of model development and the clinical relevance of the evaluation as important considerations to improve the clinical utility of the study.…”

mentioning

confidence: 99%

“…Prospective assessment will be necessary to interpret the true value of the reported model. 3 This is not intended as a criticism of the work of Wu et al, 1 who approach a novel area in dermatology using deep learning expertise, but rather aims to broach the discussion of reporting and assessment of deep learning models. Currently, the number of reports of deep learning models in medicine far exceeds their clinical usage.…”

mentioning

confidence: 99%

Reply to “A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance — simplifying the diagnosis of complicated diseases”: Machine learning evaluations need to consider the true clinical context

Steele

2021

Journal of the American Academy of Dermatology

View full text Add to dashboard Cite

show abstract

A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance: Simplifying the diagnosis of complicated diseases

Cited by 16 publications

References 3 publications

A Multimodal Deep Learning-based Platform that Assists in the Classification of Lupus Subtypes and Differential Diagnosis Among Lupus and Other Similar Skin Diseases

A Multimodal Deep Learning-based Platform that Assists in the Classification of Lupus Subtypes and Differential Diagnosis Among Lupus and Other Similar Skin Diseases

A comprehensive review on the application of artificial intelligence in drug discovery.

Reply to “A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance — simplifying the diagnosis of complicated diseases”: Machine learning evaluations need to consider the true clinical context

Contact Info

Product

Resources

About