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13 March 2024 Interactive human-machine interface for intraoperative lung cancer diagnosis using mobile optical coherence tomography and deep learning algorithms
Hsiang-Fu Huang, Rui-Cheng Zeng, Hung-Chang Liu, Chia-Wei Sun
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Proceedings Volume PC12831, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXII; PC1283108 (2024) https://doi.org/10.1117/12.3001485
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
A novel human-machine interface (HMI) combining mobile optical coherence tomography (OCT) and deep learning algorithms enables automatic identification of lung lesions during surgery. With over 80% sensitivity and specificity, this technique facilitates rapid histologically graded diagnosis, providing fast information to clinicians. It offers a cost-effective approach for early detection and treatment guidance, benefiting patients and advocating their rights in the battle against lung cancer.
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Hsiang-Fu Huang, Rui-Cheng Zeng, Hung-Chang Liu, and Chia-Wei Sun "Interactive human-machine interface for intraoperative lung cancer diagnosis using mobile optical coherence tomography and deep learning algorithms", Proc. SPIE PC12831, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXII, PC1283108 (13 March 2024); https://doi.org/10.1117/12.3001485
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KEYWORDS
Lung cancer
Optical coherence tomography
Deep learning
Evolutionary algorithms
Human-machine interfaces
Algorithm development
Cancer detection
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