Background
Automatic skin lesion recognition has shown to be effective in increasing access to reliable dermatology evaluation; however, most existing algorithms rely solely on images. Many diagnostic rules, including the 3-point checklist, are not considered by artificial intelligence algorithms, which comprise human knowledge and reflect the diagnosis process of human experts.
Objective
In this paper, we aimed to develop a semisupervised model that can not only integrate the dermoscopic features and scoring rule from the 3-point checklist but also automate the feature-annotation process.
Methods
We first trained the semisupervised model on a small, annotated data set with disease and dermoscopic feature labels and tried to improve the classification accuracy by integrating the 3-point checklist using ranking loss function. We then used a large, unlabeled data set with only disease label to learn from the trained algorithm to automatically classify skin lesions and features.
Results
After adding the 3-point checklist to our model, its performance for melanoma classification improved from a mean of 0.8867 (SD 0.0191) to 0.8943 (SD 0.0115) under 5-fold cross-validation. The trained semisupervised model can automatically detect 3 dermoscopic features from the 3-point checklist, with best performances of 0.80 (area under the curve [AUC] 0.8380), 0.89 (AUC 0.9036), and 0.76 (AUC 0.8444), in some cases outperforming human annotators.
Conclusions
Our proposed semisupervised learning framework can help with the automatic diagnosis of skin disease based on its ability to detect dermoscopic features and automate the label-annotation process. The framework can also help combine semantic knowledge with a computer algorithm to arrive at a more accurate and more interpretable diagnostic result, which can be applied to broader use cases.
Macular amyloidosis is a common type of primary localized cutaneous amyloidosis. We present a case report of a 74-year-old patient with no significant past medical history who was evaluated for dark macules and pruritus for over a year. On exam, follicular-based brown macules on the upper and lower back, bilateral shoulders, and bilateral dorsal upper arms were noted. The morphology and distribution of follicular-based macules was unusual, so the differential included follicular lichen planus, follicular eczema, and macular amyloidosis. Punch biopsy showed deposits of eosinophilic fibrillary material along with pigmentary incontinence in the papillary dermis, consistent with macular amyloidosis. Additionally, there was some trapping of the adnexal structures with atrophy of the periadnexal fat in the reticular dermis. In macular amyloidosis keratin, intermediate filaments such as cytokeratin serve as the amyloid precursors which deposit in the superficial dermis. Characteristically, macular amyloidosis presents as hyperpigmented macules or patches, often in a “rippled” linear pattern. This case highlights a rare presentation of macular amyloidosis because of the atypical follicular involvement and emphasizes the variety of presentations for localized cutaneous amyloidosis. Additionally, new treatment options such as Janus Kinase inhibitors and their potential role in the pathological pathway are discussed.
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