TY - JOUR
T1 - Automated urinary sediment detection for Fabry disease using deep-learning algorithms
AU - Uryu, Hidetaka
AU - Migita, Ohsuke
AU - Ozawa, Minami
AU - Kamijo, Chikako
AU - Aoto, Saki
AU - Okamura, Kohji
AU - Hasegawa, Fuyuki
AU - Okuyama, Torayuki
AU - Kosuga, Motomichi
AU - Hata, Kenichiro
N1 - Publisher Copyright:
© 2022
PY - 2022/12
Y1 - 2022/12
N2 - Fabry disease is a congenital lysosomal storage disease, and most of these cases develop organ damage in middle age. There are some promising therapeutic options for this disorder, which can stabilize the progression of the disease. However, a long delay in diagnosis prevents early intervention, resulting in treatment failure. Because Fabry disease is a rare disease, it is not well recognized and disease specific screening tests are rarely performed. Hence, a novel approach to for detecting patients with a widely practiced clinical test is crucial for the early detection of the disease. Recently, decision support systems based on artificial intelligence (AI) have been developed in many clinical fields. However, the construction of these models requires datasets from a large number of samples; this aspect is one of the main obstacles in AI-based approaches for rare diseases. In this study, with a novel image amplification method to construct the dataset for AI-model training, we built the deep neural-network model to detect Fabry cases from their urine samples. Sensitivity, specificity, and the AUC of the models on validation dataset were 0.902 (95% CI, 0.900–0.903), 0.977 (0.950–0.980), and 0.968 (0.964–0.972), respectively. This model could also extract disease-specific findings that are interpretable with human recognition. These results indicate that we can apply novel AI models for rare diseases based on this image amplification method we developed. We expect this approach could contribute to the diagnosis of Fabry disease. Synopsis: This is the first reported AI-based decision support system to detect undiagnosed Fabry cases, and our new image amplification method will contribute to the AI models for other rare disorders.
AB - Fabry disease is a congenital lysosomal storage disease, and most of these cases develop organ damage in middle age. There are some promising therapeutic options for this disorder, which can stabilize the progression of the disease. However, a long delay in diagnosis prevents early intervention, resulting in treatment failure. Because Fabry disease is a rare disease, it is not well recognized and disease specific screening tests are rarely performed. Hence, a novel approach to for detecting patients with a widely practiced clinical test is crucial for the early detection of the disease. Recently, decision support systems based on artificial intelligence (AI) have been developed in many clinical fields. However, the construction of these models requires datasets from a large number of samples; this aspect is one of the main obstacles in AI-based approaches for rare diseases. In this study, with a novel image amplification method to construct the dataset for AI-model training, we built the deep neural-network model to detect Fabry cases from their urine samples. Sensitivity, specificity, and the AUC of the models on validation dataset were 0.902 (95% CI, 0.900–0.903), 0.977 (0.950–0.980), and 0.968 (0.964–0.972), respectively. This model could also extract disease-specific findings that are interpretable with human recognition. These results indicate that we can apply novel AI models for rare diseases based on this image amplification method we developed. We expect this approach could contribute to the diagnosis of Fabry disease. Synopsis: This is the first reported AI-based decision support system to detect undiagnosed Fabry cases, and our new image amplification method will contribute to the AI models for other rare disorders.
KW - Artificial intelligence
KW - Deep learning
KW - Fabry disease
KW - Image augmentation
KW - Mulberry cells
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U2 - 10.1016/j.ymgmr.2022.100921
DO - 10.1016/j.ymgmr.2022.100921
M3 - Article
C2 - 36186840
AN - SCOPUS:85139019902
SN - 2214-4269
VL - 33
JO - Molecular Genetics and Metabolism Reports
JF - Molecular Genetics and Metabolism Reports
M1 - 100921
ER -