Annals of clinical and translational neurology · 2025
Deep Learning-Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy.
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Rabah Chaima Ben, Petropoulos Ioannis N, Stettner Mark, Ferdousi Maryam, Alam Uazman, Efron Nathan, Serag Ahmed, Malik Rayaz A
Аннотация
OBJECTIVE: Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning-based multiclass classification system for rapid, automated diagnosis and differentiation of 88 patients with diabetic peripheral neuropathy (DPN), chemotherapy-induced peripheral neuropathy (CIPN), chronic inflammatory demyelinating polyneuropathy (CIDP), and human immunodeficiency virus-associated sensory neuropathy (HIV-SN).
METHODS: A deep learning-based multiclass system was developed to analyze corneal nerve images. These images were preprocessed to train and validate the proposed model and the diagnostic utility was evaluated from the accuracy, F1-score and area under the curve to derive sensitivity, specificity and precision.
RESULTS: NeuropathAI achieved excellent results: AUC-96.75%, sensitivity-83.87%, specificity-95.07%, and demonstrated excellent discrimination for CIDP, CIPN, HIV-SN and DPN with one-vs-all AUC scores of 97%, 93.1%, 99.7% and 96.9%, respectively. Explainability visualization through heatmaps demonstrated that regions of decision making by the model localized to areas with nerve fiber loss, enhancing interpretability.
INTERPRETATION: NeuropathAI achieved rapid and accurate diagnosis of four of the most prevalent peripheral neuropathies globally, underscoring the potential of artificial intelligence-driven corneal image analysis for the rapid diagnosis and differentiation of peripheral neuropathies.
METHODS: A deep learning-based multiclass system was developed to analyze corneal nerve images. These images were preprocessed to train and validate the proposed model and the diagnostic utility was evaluated from the accuracy, F1-score and area under the curve to derive sensitivity, specificity and precision.
RESULTS: NeuropathAI achieved excellent results: AUC-96.75%, sensitivity-83.87%, specificity-95.07%, and demonstrated excellent discrimination for CIDP, CIPN, HIV-SN and DPN with one-vs-all AUC scores of 97%, 93.1%, 99.7% and 96.9%, respectively. Explainability visualization through heatmaps demonstrated that regions of decision making by the model localized to areas with nerve fiber loss, enhancing interpretability.
INTERPRETATION: NeuropathAI achieved rapid and accurate diagnosis of four of the most prevalent peripheral neuropathies globally, underscoring the potential of artificial intelligence-driven corneal image analysis for the rapid diagnosis and differentiation of peripheral neuropathies.