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In Journal of biophotonics

Intraoperative guidance tools for thyroid surgery based on optical coherence tomography (OCT) could aid distinguish between normal and diseased tissue. However, OCT images are difficult to interpret, thus, real-time automatic analysis could support the clinical decision making. In this study, several deep learning models were investigated for thyroid disease classification on 2D and 3D OCT data obtained from ex vivo specimens of 22 patients undergoing surgery and diagnosed with several thyroid pathologies. Additionally, two open-access datasets were used to evaluate the custom models. On the thyroid dataset, the best performance was achieved by the 3D vision transformer model with a Matthew's Correlation Coefficient (MCC) of 0.79 (accuracy 0.90) for the normal-vs-abnormal classification. On the open-access datasets, the custom models achieved the best performance (MCC>0.88, accuracy>0.96). Results obtained for the normal-vs-abnormal classification suggest OCT, complemented with deep learning-based analysis, as a tool for real-time automatic diseased tissue identification in thyroid surgery. This article is protected by copyright. All rights reserved.

Tampu Iulian Emil, Eklund Anders, Johansson Kenth, Gimm Oliver, Haj-Hosseini Neda


\nconvolutional neural networks, \noptical coherence tomography, \nsurgical guidance, \nthyroid, \ntissue classification, \nvision transformers