Application of Deep Learning-Based Multimodal Data Fusion for the Diagnosis of Skin Neglected Tropical Diseases: Systematic Review.
BACKGROUND: Neglected tropical diseases (NTDs) are the most prevalent diseases and comprise 21 different conditions. One-half of these conditions have skin manifestations, known as skin NTDs. The diagnosis of skin NTDs incorporates visual examination of patients, and deep learning (DL)-based diagnostic tools can be used to assist the diagnostic procedures. The use of advanced DL-based methods, including multimodal data fusion (MMDF) functionality, could be a potential approach to enhance the diagnostic procedures of these diseases. However, little has been done toward the application of such tools, as confirmed by the very few studies currently available that implemented MMDF for skin NTDs.
OBJECTIVE: This article presents a systematic review regarding the use of DL-based MMDF methods for the diagnosis of skin NTDs and related diseases (non-NTD skin diseases), including the ethical risks and potential risk of bias.
METHODS: The review was conducted based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method using 6 parameters (research approach followed, disease[s] diagnosed, dataset[s] used, algorithm[s] applied, performance achieved, and future direction[s]).
RESULTS: Initially, 437 articles were collected from 5 major groups of identified sources; 14 articles were selected for the final analysis. Results revealed that, compared with traditional methods, the MMDF methods improved model performances for the diagnoses of skin NTDs and non-NTD skin diseases. Algorithmically, convolutional neural network (CNN)-based models were the predominantly used DL architectures (9/14 studies, 64% ), providing feature extraction, feature fusion, and disease classification, which were also conducted with transformer-based methods (1/14, 7%). Furthermore, recurrent neural networks were used in combination with CNN-based feature extractors to fuse multimodal data (1/14, 7%) and with generative models (1/14, 7%). The remaining studies used study-specific algorithms using transformers (1/14, 7%) and generative models (1/14, 7%).
CONCLUSIONS: Finally, this article suggests that further studies should be conducted about using DL-based MMDF methods for skin NTDs, considering model efficiency, data scarcity, algorithm selection and use, fusion strategies of multiple modalities, and the possible adoption of such tools for resource-constrained areas.