Deep learning-based system for automatic identification of benign and malignant eyelid tumours

The deep learning-based system shows significant potential in improving the diagnosis of eyelid tumours, providing a reliable and efficient tool for clinical practice. Future work will validate the model with more extensive and diverse datasets and integrate it into clinical workflows for real-time diagnostic support.

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Publication Details

Journal
British Journal of Ophthalmology
Published
2025-05-10
DOI
https://doi.org/10.1136/bjo-2025-327127
Primary Topic
Nonmelanoma Skin Cancer Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Deep learning-based system for automatic identification of benign and malignant eyelid tumours

Ludwig M. Heindl, Martine J. Jager, Wanlin Fan, Weiwei Dai
British Journal of Ophthalmology
Nonmelanoma Skin Cancer Studies
article

Deep learning-based system for automatic identification of benign and malignant eyelid tumours

Ludwig M. Heindl, Martine J. Jager, Wanlin Fan, Weiwei Dai
article en

Abstract

The deep learning-based system shows significant potential in improving the diagnosis of eyelid tumours, providing a reliable and efficient tool for clinical practice. Future work will validate the model with more extensive and diverse datasets and integrate it into clinical workflows for real-time diagnostic support.

British Journal of OphthalmologyVol. 109(9)
University of Cologne (DE), Leiden University Medical Center (NL), University Hospital Cologne (DE), He Eye Hospital (CN)
Openalex Percentile: Top 85%
Nonmelanoma Skin Cancer Studies
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