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Page 421 Talwar et al. Art Int Surg. 2025;5:418-24 https://dx.doi.org/10.20517/ais.2025.22

To further refine predictive modeling, Wilson et al. compared three DL models - one trained on structured
clinical data alone, one trained on CT imaging, and one combining both datasets to predict
[10]
complications . The clinical-only DL model, incorporating variables such as age, BMI, diabetes history,
[10]
and tobacco use, outperformed both the image-only and mixed models . However, the researchers noted
that the small sample size and unbalanced dataset likely contributed to the underperformance of the image-
[10]
based models in predicting hernia recurrence . Future work should focus on identifying image-based
features that enhance predictive accuracy and ensuring the use of larger, more diverse datasets for model
training. Despite these challenges, DL applications in VHR should be further explored to augment
preoperative planning. Table 1 summarizes the studies reviewed in this section, comparing model types,
tasks, training data, and key findings.

Intraoperative decision support
Augmented reality (AR) systems that integrate imaging and AI offer an avenue for improving intraoperative
decision making in VHR. While the use of AR in VHR is nascent, real-time AI-based imaging has shown
success in related surgical applications, highlighting its potential for future integration.

Bamba et al. developed a CNN-based CV system for real-time detection of the GI tract, blood, vessels,
gauze, and clips . The system was trained on annotated surgical videos from various procedures, including
[11]
colectomy, hernia repair, and sigmoid resection, with experts manually labeling each visible tool and
anatomical object . A total of 1,070 images were utilized for training. The model, developed using IBM
[11]
Virtual Insights, demonstrated a mean sensitivity and positive predictive value (PPV) of 92% and 80%,
respectively . When identifying blood vessels - anatomy of particular relevance to VHR - the model
[11]
achieved sensitivity and PPV of 79.3% and 82.1%, respectively . Notably, however, the group noted that the
[11]
CNN model frequently misclassified reddish fat as blood vessels, suggesting the need for further refinement.

The study represents a significant step toward real-time AI-based imaging in intraoperative settings. A key
limitation of this system is that it was trained on endoscopic images, which differ from the open surgical
approach used in VHR. While the model performs well in confined endoscopic settings, its efficacy in open
surgeries, where lighting conditions and perspectives are variable, remains unverified. To address these
limitations, hybrid imaging approaches that integrate infrared or depth-sensing modalities may enhance
anatomic differentiation. By building upon current advancements, AI-powered imaging can significantly
enhance intraoperative decision making and improve patient outcomes.

Postoperative monitoring after VHR
AI-based imaging applications have the potential to enhance postoperative monitoring following VHR by
detecting wound complications [12-15] . Rochon et al. developed an AI-powered tool to identify surgical
[15]
wounds requiring priority review . The AI utilized was “You Only Look Once’ (YOLO), an object
detection algorithm known for real-time processing capabilities . While YOLO requires significant
[15]
computational power, it offers faster processing than traditional CNN models. It was trained on 37,974
images and tested on 3,634 images . The model categorized images based on the presence of four factors:
[15]
discoloration, unexpected fluids or tissues, retained sutures or clips, and incisional separation.

The model demonstrated an overall sensitivity of 89% and 100% intra-rater reliability in identifying wounds
requiring urgent review . However, accuracy fell for detecting incision separation and skin discoloration in
[15]
darker skin tones, likely due to dataset imbalance (71% lighter skin, 29% darker skin tones) . YOLO’s high
[15]
computational demands may also limit adoption. Despite these limitations, AI-driven wound assessment
presents a promising avenue for postoperative VHR monitoring.