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.

BACKGROUND:
Visualization of key anatomical landmarks is required during surgical Trans Abdominal Pre Peritoneal repair (TAPP) of inguinal hernia. The Critical View of the MyoPectineal Orifice (CVMPO) was proposed to ensure correct dissection. An artificial intelligence (AI) system that automatically validates the presence of key and marks during the procedure is a critical step towards automatic dissection quality assessment and video-based competency evaluation. The aim of this study was to develop an AI system that automatically recognizes the TAPP key CVMPO landmarks in hernia repair videos.

METHODS:
Surgical videos of 160 TAPP procedures were used in this single-center study. A deep neural network-based object detector was developed to automatically recognize the pubic symphysis, direct hernia orifice, Cooper’s ligament, the iliac vein, triangle of Doom, deep inguinal ring, and iliopsoas muscle. The system was trained using 130 videos, annotated and verified by two board-certified surgeons. Performance was evaluated in 30 videos of new patients excluded from the training data.

RESULTS:
Performance was validated in 2 ways first, single-image validation where the AI model detected landmarks in a single laparoscopic image (mean average precision (MAP) of 51.2%). The second validation is video evaluation where the model detected landmarks throughout the myopectineal orifice visual inspection phase (mean accuracy and F-score of 77.1 and 75.4% respectively). Annotation objectivity was assessed between 2 surgeons in video evaluation, showing a high agreement of 88.3%.

CONCLUSION:
This study establishes the first AI-based automated recognition of critical structures in TAPP surgical videos, and a major step towards automatic CVMPO validation with AI. Strong performance was achieved in the video evaluation. The high inter-rater agreement confirms annotation quality and task objectivity.

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.

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.

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.

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.

BACKGROUND:
The large amount of heterogeneous data collected in surgical/endoscopic practice calls for data-driven approaches as machine learning (ML) models. The aim of this study was to develop ML models to predict endoscopic sleeve gastroplasty (ESG) efficacy at 12 months defined by total weight loss (TWL) % and excess weight loss (EWL) % achievement. Multicentre data were used to enhance generalizability evaluate consistency among different center of ESG practice and assess reproducibility of the models and possible clinical application. Models were designed to be dynamic and integrate follow-up clinical data into more accurate predictions, possibly assisting management and decision-making.

METHODS:
ML models were developed using data of 404 ESG procedures performed at 12 centers across Europe. Collected data included clinical and demographic variables at the time of ESG and at follow-up. Multicentre/external and single center/internal and temporal validation were performed. Training and evaluation of the models were performed on Python’s scikit-learn library. Performance of models was quantified as receiver operator curve (ROC-AUC), sensitivity, specificity, and calibration plots.

RESULTS:
Multicenter external validation ML models using preoperative data show poor performance. Best performances were reached by linear regression (LR) and support vector machine models for TWL% and EWL%, respectively, (ROC-AUC TWL% 0.87, EWL% 0.86) with the addition of 6-month follow-up data. Single-center internal validation Preoperative data only ML models show suboptimal performance. Early, i.e., 3-month follow-up data addition lead to ROC-AUC of 0.79 (random forest classifiers model) and 0.81 (LR models) for TWL% and EWL% achievement prediction, respectively. Single-center temporal validation shows similar results.

CONCLUSIONS:
Although preoperative data only may not be sufficient for accurate postoperative predictions, the ability of ML models to adapt and evolve with the patients changes could assist in providing an effective and personalized postoperative care. ML models predictive capacity improvement with follow-up data is encouraging and may become a valuable support in patient management and decision-making.