Abstract
Biological tissue identification in real clinical scenarios is a relevant and unsolved medical problem, particularly in the operating room. Although it could be thought that healthy tissue identification is an immediate task, in practice there are several clinical situations that greatly impede this process. For instance, it could be challenging in open surgery in complex areas, such as the neck, where different structures are quite close together, with bleeding and other artifacts affecting visual inspection. Solving this issue requires, on one hand, a high contrast noninvasive technique and, on the other hand, powerful classification algorithms. Regarding the technique, optical diffuse reflectance spectroscopy has demonstrated such capabilities in the discrimination of tumoral and healthy biological tissues. The complex signals obtained, in the form of spectra, need to be adequately computed in order to extract relevant information for discrimination. As usual, accurate discrimination relies on massive measurements, some of which serve as training sets for the classification algorithms. In this work, diffuse reflectance spectroscopy is proposed, implemented, and tested as a potential technique for healthy tissue discrimination. A specific setup is built and spectral measurements on several ex vivo porcine tissues are obtained. The massive data obtained are then analyzed for classification purposes. First of all, considerations about normalization, detrending and noise are taken into account. Dimensionality reduction and tendencies extraction are also considered. Featured spectral characteristics, principal component or linear discrimination analysis are applied, as long as classification approaches based on k-nearest neighbors (k-NN), quadratic discrimination analysis (QDA) or Naïve Bayes (NB). Relevant parameters about classification accuracy are obtained and compared, including ANOVA tests. The results show promising values of specificity and sensitivity of the technique for some classification algorithms, even over 95%, which could be relevant for clinical applications in the operating room.
Highlights
The results show promising values of specificity and sensitivity of the technique for some classification algorithms, even over 95%, which could be relevant for clinical applications in the operating room
Biological tissue identification in the operating room is critical in many situations, in tight volumetric spaces with bleeding and illumination artifacts
The subsequent application of classification algorithms, whose main results are in Table 3, showed a maximum accuracy of almost 95% for the k-nearest neighbors (k-NN) approach
Summary
Biological tissue identification in the operating room is critical in many situations, in tight volumetric spaces with bleeding and illumination artifacts. In these scenarios the identification cannot be reliably made by sight. An incorrect identification of a biological tissue can result in nerve or blood vessel resection for instance, with undesired partial paralysis or hemorrhage. It is essential to provide healthy tissue type feedback in the operating room to avoid collateral damage. The contributions to the solution of this problem imply a noninvasive high contrast technique, and powerful classification algorithms to provide accurate results. Regarding the noninvasive high contrast technique, optical radiation provides noninvasive, nonionizing procedures, even noncontact
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