Human-multimodal deep learning collaboration in 'precise' diagnosis of lupus erythematosus subtypes and similar skin diseases
- PMID: 38619440
- DOI: 10.1111/jdv.20031
Human-multimodal deep learning collaboration in 'precise' diagnosis of lupus erythematosus subtypes and similar skin diseases
Abstract
Background: Lupus erythematosus (LE) is a spectrum of autoimmune diseases. Due to the complexity of cutaneous LE (CLE), clinical skin image-based artificial intelligence is still experiencing difficulties in distinguishing subtypes of LE.
Objectives: We aim to develop a multimodal deep learning system (MMDLS) for human-AI collaboration in diagnosis of LE subtypes.
Methods: This is a multi-centre study based on 25 institutions across China to assist in diagnosis of LE subtypes, other eight similar skin diseases and healthy subjects. In total, 446 cases with 800 clinical skin images, 3786 multicolor-immunohistochemistry (multi-IHC) images and clinical data were collected, and EfficientNet-B3 and ResNet-18 were utilized in this study.
Results: In the multi-classification task, the overall performance of MMDLS on 13 skin conditions is much higher than single or dual modals (Sen = 0.8288, Spe = 0.9852, Pre = 0.8518, AUC = 0.9844). Further, the MMDLS-based diagnostic-support help improves the accuracy of dermatologists from 66.88% ± 6.94% to 81.25% ± 4.23% (p = 0.0004).
Conclusions: These results highlight the benefit of human-MMDLS collaborated framework in telemedicine by assisting dermatologists and rheumatologists in the differential diagnosis of LE subtypes and similar skin diseases.
© 2024 European Academy of Dermatology and Venereology.
References
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Grants and funding
- 2018XK2304/Key Research and Development Program of Hunan Province, China
- 2021SK53509/Clinical Medical Innovation Guide Project of Hunan Province
- 2019RS2012/Hunan Talent Young Investigator of China
- 2020-RC320-003/Nonprofit Central Research Institute Fund of the Chinese Academy of Medical Sciences
- 2021-RC320-001/Nonprofit Central Research Institute Fund of the Chinese Academy of Medical Sciences
- 2020JJ2055/Hunan Outstanding Young Investigator of China
- 61972333/National Natural Science Foundation of China
- 81773332/National Natural Science Foundation of China
- 81830097/National Natural Science Foundation of China
- 81972943/National Natural Science Foundation of China
- 82173425/National Natural Science Foundation of China
- 2019-I2 M-5-033/CAMS Innovation Fund for Medical Sciences (CIFMS)
- 2021-I2 M-1-059/CAMS Innovation Fund for Medical Sciences (CIFMS)
- 2021YFC2702004/National Key R&D Program of China
- kh2202005/Science and Technology Major Project of Changsha
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