# A Scoping Review of the Photographic Assessment of Donor Liver Steatosis in Transplantation Using Artificial Intelligence

## TL;DR

This paper reviews how AI can help assess liver quality in transplants using photos, finding some promise but highlighting the need for better validation.

## Contribution

A scoping review of AI tools for liver steatosis assessment using photographs, identifying gaps in validation and dataset robustness.

## Key findings

- Six studies from three groups showed AI accuracy ranging from 0.81 to 0.92 for liver steatosis assessment.
- Most studies used a 30% steatosis threshold with binary classification models.
- Common challenges included small datasets and lack of external validation.

## Abstract

Accurate evaluation of liver steatosis and overall organ quality is critical for optimizing safe organ utilization in liver transplantation. Recent advances in computer vision offer promising tools to standardize and enhance this process. This review maps the current evidence on AI‐enabled photographic evaluation of liver steatosis and identifies areas for future development.

A scoping review of the literature, including searches of PubMed, SCOPUS, and Web of Science, was conducted to identify studies published from inception to 27/03/2025 reporting on the development of AI‐enabled tools for assessing liver organ quality from photographs taken during the donation process. A qualitative synthesis and critical review of the literature was conducted in accordance with PRISMA‐ScR guidelines. The review protocol was registered with the Open Science Framework (osf.io/zfcuk).

After screening 219 citations, six studies from three independent research groups met the inclusion criteria. Sample sizes ranged from 40 to 192 donors. Five studies employed binary classification models using a 30% steatosis threshold, while one study reported a graded approach. Reported accuracies ranged from 0.81 to 0.92. Common challenges included small and imbalanced datasets with a dependence on supplementary donor data, such as blood tests and radiological findings. None of the studies conducted external validation.

Current evidence is drawn from a small and methodologically heterogeneous literature. Publications from several independent groups nevertheless highlight growing interest in developing these tools. Future work should prioritize larger studies with robust external validation to strengthen their credibility and build trust in their clinical use.