A coaxial excitation, dual-red-green-blue/near-infrared paired imaging system toward computer-aided detection of parathyroid glands in situ and ex vivo.
A coaxial excitation, dual-red-green-blue/near-infrared paired imaging system toward computer-aided detection of parathyroid glands in situ and ex vivo.
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DOI：
10.1002/jbio.202200008
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发表时间：
2022-08
影响因子：
2.8
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甲状旁腺（PGs）由于其体积小且与周围组织外观相似，在甲状腺切除术中早期准确检测是一个具有挑战性的问题。近红外自体荧光（NIRAF）作为一种定位pg的方法引起了人们的兴趣。然而，据报道，这种技术对pg的假阳性发生率很高。我们介绍了一个配备了同轴激发光（785-nm）和双传感器的原型，以解决NIRAF技术的假阳性问题。我们测试了我们的原型在体内和离体的临床可行性使用无菌帷幕在10人的对象。收集检测到的PG的视频数据（1,287张图像），用于训练、验证和比较PG检测的性能。我们实现了94.7%的平均精度和19.5毫秒的处理时间/检测。这项可行性研究支持了光学设计的有效性，并可能为基于深度学习的PG检测方法打开新的大门。本文展示了一种同轴激励，双红绿蓝(RGB)/近红外（NIR）配对成像系统的初步可行性，该系统可以在术中检测甲状旁腺的自身荧光信号，并利用计算机辅助算法在事后定位它们。该研究的目的是探索解决当前近红外技术的假阴性/阳性问题的潜力。我们的机器学习算法在6例甲状腺/甲状旁腺切除术患者的实时数据上进行了测试，平均精度为94.7%，每次检测的处理时间为19.5毫秒。
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 (1,287 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. This paper shows the preliminary feasibility of a co-axial excitation, dual-red-green-blue (RGB)/near-infrared (NIR) paired imaging system that detects autofluorescence signals from parathyroid glands intraoperatively and exploits computer-aided algorithms to localize them post-hoc. The aim of the study was to explore the potential of addressing false negative/positive issues from current NIR technology. Our machine learning algorithm was tested on real-time data from 6 thyroid/parathyroidectomy patients and achieved a mean average precision of 94.7% and a 19.5 millisecond processing time per detection.