Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning.

Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm "peak temperature prediction model" (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment

Partial Hepatic Vein Occlusion and Venous Congestion in Liver Exploration Using a Hyperspectral Camera: A Proposal for Monitoring Intraoperative Liver Perfusion.

INTRODUCTION The changes occurring in the liver in cases of outflow deprivation have rarely been investigated, and no measurements of this phenomenon are available. This investigation explored outflow occlusion in a pig model using a hyperspectral camera. METHODS Six pigs were enrolled. The right hepatic vein was clamped for 30 min. The oxygen saturation (StO2%), deoxygenated hemoglobin level (de-Hb), near-infrared perfusion (NIR), and total hemoglobin index (THI) were investigated at different time points in four perfused lobes using a hyperspectral camera measuring light absorbance between 500 nm and 995 nm. Differences among lobes at different time points were estimated by mixed-effect linear regression. RESULTS StO2% decreased over time in the right lateral lobe (RLL, totally occluded) when compared to the left lateral (LLL, outflow preserved) and the right medial (RML, partially occluded) lobes (p < 0.05). De-Hb significantly increased after clamping in RLL when compared to RML and LLL (p < 0.05). RML was further analyzed considering the right portion (totally occluded) and the left portion of the lobe (with an autonomous draining vein). StO2% decreased and de-Hb increased more smoothly when compared to the totally occluded RLL (p < 0.05). CONCLUSIONS The variations of StO2% and deoxy-Hb could be considered good markers of venous liver congestion

HIV sero-positivity and risk factors for ischaemic and haemorrhagic stroke in hospitalised patients in Uganda : A prospective-case-control study

OBJECTIVES: We examined HIV sero-positivity and risk factors in patients admitted with ischaemic stroke (IS) and haemorrhagic stroke (HS) in Kampala, Uganda. STUDY DESIGN: We conducted a matched case-control study between December 2016 and December 2018 at St Francis Hospital, Nsambya. METHODS: The study population comprised of stroke cases (adults aged ?18 years with IS or HS confirmed by neuroimaging) and controls (age- and sex-matched stroke-free adults aged ?18 years who were recruited from the same hospital as the cases). A comprehensive assessment for sociodemographic, lifestyle and clinical factors was performed using the World Health Organization (WHO) STEP-wise approach to Surveillance (STEPS) for stroke risk factor surveillance. We used conditional logistic regression to identify risk factors associated with IS or HS. RESULTS: We enrolled 137 matched case-control pairs; 48 (35 were men, and the mean ages were 62.4 years (SD $14.8) for cases and 61.1 years (SD$ 14.1) for controls. Of stroke patients, 86 (63 had IS and 51 (37 had HS. Overall, HIV sero-positivity was 10positivity was not significantly associated with stroke (unadjusted odds ratio [uOR] = 1.49, 95CI] 0.59?3.78). A self-reported family history of diabetes mellitus was associated with an increased risk of all stroke (adjusted odds ratio [aOR] = 4.41, 95.47?13.2), as well as for IS and HS separately (aOR = 3.66, 95.09?12.4 and aOR = 4.99, 95.02?24.4, respectively). High blood pressure (?140/90 mmHg) was associated with an increased risk of all stroke (aOR = 12.3, 952?44.1), and this was also true for IS and HS individually (aOR = 6.48, 95.15?36.7 and aOR = 5.63, 95.74?18.2, respectively). CONCLUSIONS: No association was found between HIV sero-positivity and stroke occurrence among Ugandan stroke patients. Hypertension and a self-reported family history of diabetes mellitus were significant risk factors for both IS and HS. Interventions to reduce hypertension and diabetes mellitus in the Ugandan population are urgently required. Much larger studies are required to demonstrate if any association exists between HIV and stroke

Deep learning analysis of in vivo hyperspectral images for automated intraoperative nerve detection

Nerves are critical structures that may be difficult to recognize during surgery. Inadvertent nerve injuries can have catastrophic consequences for the patient and lead to life-long pain and a reduced quality of life. Hyperspectral imaging (HSI) is a non-invasive technique combining photography with spectroscopy, allowing non-invasive intraoperative biological tissue property quantification. We show, for the first time, that HSI combined with deep learning allows nerves and other tissue types to be automatically recognized in in vivo hyperspectral images. An animal model was used, and eight anesthetized pigs underwent neck midline incisions, exposing several structures (nerve, artery, vein, muscle, fat, skin). State-of-the-art machine learning models were trained to recognize these tissue types in HSI data. The best model was a convolutional neural network (CNN), achieving an overall average sensitivity of 0.91 and a specificity of 1.0, validated with leave-one-patient-out cross-validation. For the nerve, the CNN achieved an average sensitivity of 0.76 and a specificity of 0.99. In conclusion, HSI combined with a CNN model is suitable for in vivo nerve recognition

Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging.

Hyperspectral imaging (HSI) is a non-invasive imaging modality already applied to evaluate hepatic oxygenation and to discriminate different models of hepatic ischemia. Nevertheless, the ability of HSI to detect and predict the reperfusion damage intraoperatively was not yet assessed. Hypoxia caused by hepatic artery occlusion (HAO) in the liver brings about dreadful vascular complications known as ischemia-reperfusion injury (IRI). Here, we show the evaluation of liver viability in an HAO model with an artificial intelligence-based analysis of HSI. We have combined the potential of HSI to extract quantitative optical tissue properties with a deep learning-based model using convolutional neural networks. The artificial intelligence (AI) score of liver viability showed a significant correlation with capillary lactate from the liver surface (r = -0.78, p = 0.0320) and Suzuki's score (r = -0.96, p = 0.0012). CD31 immunostaining confirmed the microvascular damage accordingly with the AI score. Our results ultimately show the potential of an HSI-AI-based analysis to predict liver viability, thereby prompting for intraoperative tool development to explore its application in a clinical setting

Computer-Assisted Differentiation between Colon-Mesocolon and Retroperitoneum Using Hyperspectral Imaging (HSI) Technology

Complete mesocolic excision (CME), which involves the adequate resection of the tumor-bearing colonic segment with "en bloc" removal of its mesocolon along embryological fascial planes is associated with superior oncological outcomes. However, CME presents a higher complication rate compared to non-CME resections due to a higher risk of vascular injury. Hyperspectral imaging (HSI) is a contrast-free optical imaging technology, which facilitates the quantitative imaging of physiological tissue parameters and the visualization of anatomical structures. This study evaluates the accuracy of HSI combined with deep learning (DL) to differentiate the colon and its mesenteric tissue from retroperitoneal tissue. In an animal study including 20 pig models, intraoperative hyperspectral images of the sigmoid colon, sigmoid mesentery, and retroperitoneum were recorded. A convolutional neural network (CNN) was trained to distinguish the two tissue classes using HSI data, validated with a leave-one-out cross-validation process. The overall recognition sensitivity of the tissues to be preserved (retroperitoneum) and the tissues to be resected (colon and mesentery) was 79.0 ± 21.0% and 86.0 ± 16.0%, respectively. Automatic classification based on HSI and CNNs is a promising tool to automatically, non-invasively, and objectively differentiate the colon and its mesentery from retroperitoneal tissue

Surgical optomics: hyperspectral imaging and deep learning towards precision intraoperative automatic tissue recognition-results from the EX-MACHYNA trial.

BACKGROUND Hyperspectral imaging (HSI), combined with machine learning, can help to identify characteristic tissue signatures enabling automatic tissue recognition during surgery. This study aims to develop the first HSI-based automatic abdominal tissue recognition with human data in a prospective bi-center setting. METHODS Data were collected from patients undergoing elective open abdominal surgery at two international tertiary referral hospitals from September 2020 to June 2021. HS images were captured at various time points throughout the surgical procedure. Resulting RGB images were annotated with 13 distinct organ labels. Convolutional Neural Networks (CNNs) were employed for the analysis, with both external and internal validation settings utilized. RESULTS A total of 169 patients were included, 73 (43.2%) from Strasbourg and 96 (56.8%) from Verona. The internal validation within centers combined patients from both centers into a single cohort, randomly allocated to the training (127 patients, 75.1%, 585 images) and test sets (42 patients, 24.9%, 181 images). This validation setting showed the best performance. The highest true positive rate was achieved for the skin (100%) and the liver (97%). Misclassifications included tissues with a similar embryological origin (omentum and mesentery: 32%) or with overlaying boundaries (liver and hepatic ligament: 22%). The median DICE score for ten tissue classes exceeded 80%. CONCLUSION To improve automatic surgical scene segmentation and to drive clinical translation, multicenter accurate HSI datasets are essential, but further work is needed to quantify the clinical value of HSI. HSI might be included in a new omics science, namely surgical optomics, which uses light to extract quantifiable tissue features during surgery