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Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence.

Chol Min Kang¹, Hyung June Ku², Hyung Hwan Moon^2,3

¹Department of Applied Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21287, USA.

Journal of Clinical Medicine

|January 23, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) improves predicting safe liver resection volumes, reducing the risk of post-hepatectomy liver failure (PHLF). This AI tool enhances surgical planning for major hepatectomies, leading to better patient outcomes.

Keywords:

CT volumetry artificial intelligence major hepatectomy postoperative liver failure right hemi-hepatectomy

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Area of Science:

Hepatobiliary surgery
Medical artificial intelligence
Surgical oncology

Background:

Precise preoperative liver function assessment is crucial to prevent post-hepatectomy liver failure (PHLF).
Major hepatectomies require accurate determination of safe resection volumes.
Existing methods for assessing liver function and resection safety have limitations.

Purpose of the Study:

To introduce a novel artificial intelligence (AI) application for determining safe liver resection volumes.
To enhance preoperative planning for major hepatectomies using AI.
To improve patient outcomes by minimizing the risk of PHLF.

Main Methods:

A deep learning model with a liver-specific loss function was developed.
Patient data including characteristics, lab results, and CT-based liver volumetry were analyzed.
The AI approach was benchmarked against traditional and other machine learning techniques.

Main Results:

The AI model achieved 68.8% accuracy in predicting safe resection volumes.
This performance surpassed existing machine and deep learning models.
The mean absolute error in under-predicted volumes was significantly reduced to 23.72.

Conclusions:

AI integration offers a more precise method for estimating safe liver resection limits.
This approach has the potential to significantly reduce PHLF rates.
The study highlights AI's role in optimizing surgical planning for liver resections.