Background. Hyperspectral imaging (HSI) is a relatively new method used in image-26 guided and precision surgery, which has shown promising results for characterization 27 of tissues and assessment of physiologic tissue parameters. Previous methods used 28 for analysis of preconditioning concepts in patients and animal models have shown 29 several limitations of application. The aim of this study was to evaluate HSI for the 30 measurement of ischemic conditioning effects during esophagectomy. 31 Methods. Intraoperative hyperspectral images of the gastric tube through the mini-32 thoracotomy were recorded from n=22 patients, 14 of whom underwent laparoscopic 33 gastrolysis and ischemic conditioning of the stomach with two-step transthoracic 34 esophagectomy and gastric pull-up with intrathoracic anastomosis after 3-7 days. 35 The tip of the gastric tube (later esophago-gastric anastomosis) was measured with 36 HSI. Analysis software provides a RGB image and 4 false color images representing 37 physiologic parameters of the recorded tissue area intraoperatively. These parameters contain tissue oxygenation (StO2), perfusion-(NIR Perfusion Index), 1 organ hemoglobin-(OHI) and tissue water index (TWI). 2 Results. Intraoperative HSI of the gastric conduit was possible in all patients and did 3 not prolong the regular operative procedure due to its quick applicability. In particular, 4 the tissue oxygenation of the gastric conduit was significantly higher in patients who 5 underwent ischemic conditioning (StO2Precond. = 78%; StO2NoPrecond. = 66%; p = 0.03). Conclusions. HSI is suitable for contact-free, non-invasive and intraoperative 7 evaluation of physiological tissue parameters within gastric conduits. Therefore HSI is 8 a valuable method for evaluating ischemic conditioning effects and may contribute to 9 reduce anastomotic complications. Additional studies are needed to establish normal 10 values and thresholds of the presented parameters for the gastric conduit 11 anastomotic site.

Significance: Hyperspectral imaging (HSI) can support intraoperative perfusion assessment, the identification of tissue structures, and the detection of cancerous lesions. The practical use of HSI for minimal-invasive surgery is currently limited, for example, due to long acquisition times, missing video, or large setups. Aim: An HSI laparoscope is described and evaluated to address the requirements for clinical use and high-resolution spectral imaging. Approach: Reflectance measurements with reference objects and resected human tissue from 500 to 1000 nm are performed to show the consistency with an approved medical HSI device for open surgery. Varying object distances are investigated, and the signal-to-noise ratio (SNR) is determined for different light sources. Results: The handheld design enables real-time processing and visualization of HSI data during acquisition within 4.6 s. A color video is provided simultaneously and can be augmented with spectral information from push-broom imaging. The reflectance data from the HSI system for open surgery at 50 cm and the HSI laparoscope are consistent for object distances up to 10 cm. A standard rigid laparoscope in combination with a customized LED light source resulted in a mean SNR of 30 to 43 dB (500 to 950 nm). Conclusions: Compact and rapid HSI with a high spatial-and spectral-resolution is feasible in clinical practice. Our work may support future studies on minimally invasive HSI to reduce intraand postoperative complications.

The HSI method provides a non-contact, non-invasive, intraoperative imaging procedure without the use of a contrast medium, which enables a real-time analysis of physiological anastomotic parameters, which may contribute to determine the "ideal" anastomotic region. In light of this, the establishment of this methodology in the field of visceral surgery, enabling the generation of normal or cut off values for different gastrointestinal anastomotic types, is an obvious necessity.

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Currently, colorectal cancer (CRC) is mainly identified via a visual assessment during colonoscopy, increasingly used artificial intelligence algorithms, or surgery. Subsequently, CRC is confirmed through a histopathological examination by a pathologist. Hyperspectral imaging (HSI), a non-invasive optical imaging technology, has shown promising results in the medical field. In the current study, we combined HSI with several artificial intelligence algorithms to discriminate CRC. Between July 2019 and May 2020, 54 consecutive patients undergoing colorectal resections for CRC were included. The tumor was imaged from the mucosal side with a hyperspectral camera. The image annotations were classified into three groups (cancer, CA; adenomatous margin around the central tumor, AD; and healthy mucosa, HM). Classification and visualization were performed based on a four-layer perceptron neural network. Based on a neural network, the classification of CA or AD resulted in a sensitivity of 86% and a specificity of 95%, by means of leave-one-patient-out cross-validation. Additionally, significant differences in terms of perfusion parameters (e.g., oxygen saturation) related to tumor staging and neoadjuvant therapy were observed. Hyperspectral imaging combined with automatic classification can be used to differentiate between CRC and healthy mucosa. Additionally, the biological changes induced by chemotherapy to the tissue are detectable with HSI.

Purpose One relevant aspect for anastomotic leakage in colorectal surgery is blood perfusion of both ends of the anastomosis. The clinical evaluation of this issue is limited, but new methods like fluorescence angiography with indocyanine green or non-invasive and contactless hyperspectral imaging have evolved as objective parameters for perfusion evaluation. Methods In this prospective, non-randomized, open-label and two-arm study, fluorescence angiography and hyperspectral imaging were compared in 32 consecutive patients with each other and with the clinical assessment by the surgeon. After preparation of the bowel and determination of the surgical resection line, the tissue was evaluated with hyperspectral imaging for 5 min before and after cutting the marginal artery and assessed by 6 hyperspectral pictures followed by fluorescence angiography with indocyanine green. Results In 30 of 32 patients, the image data could be evaluated and compared. Both methods provided a comparable borderline between well-perfused and poorly perfused tissue (p = 0.704). In 15 cases, the surgical resection line was shifted to the central position due to the imaging. The border zone was sharper in fluorescence angiography and best assessed 31 s after injection. With hyperspectral imaging, the border zone was visualized wider and with more differences between proximal and distal border. Conclusion Hyperspectral imaging and fluorescence angiography provide similar results in determining the perfusion border. Both methods allow a good and safe visualization of the blood perfusion at the central resection margin to create a well-perfused anastomosis. Trial registration This study was registered at Clinicaltrials.gov (NCT04226781) on January 13, 2020.

There are approximately 1.8 million diagnoses of colorectal cancer, 1 million diagnoses of stomach cancer, and 0.6 million diagnoses of esophageal cancer each year globally. An automatic computer-assisted diagnostic (CAD) tool to rapidly detect colorectal and esophagogastric cancer tissue in optical images would be hugely valuable to a surgeon during an intervention. Based on a colon dataset with 12 patients and an esophagogastric dataset of 10 patients, several state-of-the-art machine learning methods have been trained to detect cancer tissue using hyperspectral imaging (HSI), including Support Vector Machines (SVM) with radial basis function kernels, Multi-Layer Perceptrons (MLP) and 3D Convolutional Neural Networks (3DCNN). A leave-one-patient-out cross-validation (LOPOCV) with and without combining these sets was performed. The ROC-AUC score of the 3DCNN was slightly higher than the MLP and SVM with a difference of 0.04 AUC. The best performance was achieved with the 3DCNN for colon cancer and esophagogastric cancer detection with a high ROC-AUC of 0.93. The 3DCNN also achieved the best DICE scores of 0.49 and 0.41 on the colon and esophagogastric datasets, respectively. These scores were significantly improved using a patient-specific decision threshold to 0.58 and 0.51, respectively. This indicates that, in practical use, an HSI-based CAD system using an interactive decision threshold is likely to be valuable. Experiments were also performed to measure the benefits of combining the colorectal and esophagogastric datasets (22 patients), and this yielded significantly better results with the MLP and SVM models.

Background: Thyroidectomy is one of the most commonly performed surgical procedures. The region of the neck has a very complex structural organization. It would be beneficial to introduce a tool that can assist the surgeon in tissue discrimination during the procedure. One such solution is the noninvasive and contactless technique, called hyperspectral imaging (HSI). Methods: To interpret the HSI data, we implemented a supervised classification method to automatically discriminate the parathyroid, the thyroid, and the recurrent laryngeal nerve from surrounding tissue(muscle, skin) and materials (instruments, gauze). A leave-one-patient-out cross-validation was performed. Results: The best performance was obtained using support vector machine (SVM) with a classification and visualization in less than 1.4 seconds. A mean patient accuracy of 68% ± 23% was obtained for all tissues and material types. Conclusions: The proposed method showed promising results and have to be confirmed on a larger cohort of patient data.