# Automated triage of cancer-suspicious skin lesions with 3D total-body photography

## TL;DR

This paper presents a machine learning model for automatically triaging suspicious skin lesions using 3D total-body photography to improve early skin cancer detection.

## Contribution

The study introduces a model that leverages intra-patient context for improved triage of skin lesions using a large dataset from 3D total-body photos.

## Key findings

- A model using intra-patient context outperformed a prior published approach in lesion triage.
- An ablation study confirmed the clinical plausibility of automated atypical skin lesion triage.
- A global dataset of over 900,000 lesion crops was used for the ISIC 2024 machine learning challenge.

## Abstract

Careful selection of skin lesions that require expert evaluation is important for early skin cancer detection. Yet challenges include lack of cost-effective asymptomatic screening, geographical inequality in access to specialty dermatology, and long wait times due to exam inefficiencies and staff shortages. Machine learning models trained on high-quality dermoscopy photos have been shown to aid clinicians in diagnosing individual, hand-selected skin lesions. In contrast, models designed for triage have been less explored due to limited datasets that represent a broader net of skin lesions. 3D total body photography is an emerging technology used in dermatology to document all apparent skin lesions on a patient for skin cancer monitoring. A multi-institutional and global project collected over 900,000 lesion crops off 3D total body photos for an online grand challenge in machine learning. Here we summarize the results of the competition, ‘ISIC 2024 – Skin Cancer Detection with 3D-TBP’, demonstrate superiority of a model that utilized intra-patient context against a prior published approach, and explore clinical plausibility of automated atypical skin lesion triage through an ablation study.