Objective:

To improve the measurement of ICG fluorescence perfusion in neonatal intestinal surgery through advanced deep learning-based point tracking techniques.

Approach:

Key Findings:

• CoTracker3-based tracking maintained stable correspondence throughout challenging segments, unlike PerfusionTech, which struggled with non-rigid motion.
• There was near-perfect agreement for temporal metrics and strong concordance for intensity- and slope-related measures between the proposed method and PerfusionTech, indicating improved measurement reliability.

Interpretation:

The proposed method enhances the reliability of ICG fluorescence quantification and spatially resolved perfusion mapping in neonatal surgery.

Limitations:

• The evaluation was limited to a small dataset of six neonatal patients, which may affect the generalizability of the findings.
• The comparison with PerfusionTech excluded cases where ROI tracking failed, potentially biasing the results.

Conclusion:

The study demonstrates the potential of deep learning techniques to improve intraoperative assessment of bowel perfusion in neonates.

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