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Predicting the clinical management of skin lesions using deep learning
- Source :
- Scientific Reports, Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
- Publication Year :
- 2020
-
Abstract
- Automated machine learning approaches to skin lesion diagnosis from images are approaching dermatologist-level performance. However, current machine learning approaches that suggest management decisions rely on predicting the underlying skin condition to infer a management decision without considering the variability of management decisions that may exist within a single condition. We present the first work to explore image-based prediction of clinical management decisions directly without explicitly predicting the diagnosis. In particular, we use clinical and dermoscopic images of skin lesions along with patient metadata from the Interactive Atlas of Dermoscopy dataset (1011 cases; 20 disease labels; 3 management decisions) and demonstrate that predicting management labels directly is more accurate than predicting the diagnosis and then inferring the management decision ($$13.73 \pm 3.93\%$$ 13.73 ± 3.93 % and $$6.59 \pm 2.86\%$$ 6.59 ± 2.86 % improvement in overall accuracy and AUROC respectively), statistically significant at $$p < 0.001$$ p < 0.001 . Directly predicting management decisions also considerably reduces the over-excision rate as compared to management decisions inferred from diagnosis predictions (24.56% fewer cases wrongly predicted to be excised). Furthermore, we show that training a model to also simultaneously predict the seven-point criteria and the diagnosis of skin lesions yields an even higher accuracy (improvements of $$4.68 \pm 1.89\%$$ 4.68 ± 1.89 % and $$2.24 \pm 2.04\%$$ 2.24 ± 2.04 % in overall accuracy and AUROC respectively) of management predictions. Finally, we demonstrate our model’s generalizability by evaluating on the publicly available MClass-D dataset and show that our model agrees with the clinical management recommendations of 157 dermatologists as much as they agree amongst each other.
- Subjects :
- 0301 basic medicine
Computer science
Science
Dermoscopy
Machine learning
computer.software_genre
Skin Diseases
Article
030207 dermatology & venereal diseases
03 medical and health sciences
0302 clinical medicine
Text mining
Deep Learning
Image Interpretation, Computer-Assisted
Humans
Generalizability theory
Melanoma
Skin
Multidisciplinary
business.industry
Deep learning
Metadata
030104 developmental biology
Medicine
Artificial intelligence
Skin lesion
business
computer
Subjects
Details
- ISSN :
- 20452322
- Volume :
- 11
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific reports
- Accession number :
- edsair.doi.dedup.....3cf70537834f19fdb2f93f1c35527f8d