Early and precise detection of parathyroid glands (PGs) is a challenging problem in thyroidectomy due to their small size and similar appearance to surrounding tissues. Near-infrared autofluorescence (NIRAF) has stimulated interest as a method to localize PGs. However, high incidence of false positives for PGs has been reported with this technique. We introduce a prototype equipped with a coaxial excitation light (785 nm) and a dual-sensor to address the issue of false positives with the NIRAF technique. We test the clinical feasibility of our prototype in situ and ex vivo using sterile drapes on 10 human subjects. Video data (1287 images) of detected PGs were collected to train, validate and compare the performance for PG detection. We achieved a mean average precision of 94.7% and a 19.5-millisecond processing time/detection. This feasibility study supports the effectiveness of the optical design and may open new doors for a deep learning-based PG detection method.
Keywords: deep learning; hypocalcemia; near-infrared autofluorescence; parathyroid glands; thyroid surgery.
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