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Techniques in Coloproctology
Published in: Techniques in Coloproctology 1/2024

Open Access 01-12-2024 | Rectal Cancer | Original Article

Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds

Authors: D. Carter, D. Bykhovsky, A. Hasky, I. Mamistvalov, Y. Zimmer, E. Ram, O. Hoffer

Published in: Techniques in Coloproctology | Issue 1/2024

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Abstract

Background

Imaging is vital for assessing rectal cancer, with endoanal ultrasound (EAUS) being highly accurate in large tertiary medical centers. However, EAUS accuracy drops outside such settings, possibly due to varied examiner experience and fewer examinations. This underscores the need for an AI-based system to enhance accuracy in non-specialized centers. This study aimed to develop and validate deep learning (DL) models to differentiate rectal cancer in standard EAUS images.

Methods

A transfer learning approach with fine-tuned DL architectures was employed, utilizing a dataset of 294 images. The performance of DL models was assessed through a tenfold cross-validation.

Results

The DL diagnostics model exhibited a sensitivity and accuracy of 0.78 each. In the identification phase, the automatic diagnostic platform achieved an area under the curve performance of 0.85 for diagnosing rectal cancer.

Conclusions

This research demonstrates the potential of DL models in enhancing rectal cancer detection during EAUS, especially in settings with lower examiner experience. The achieved sensitivity and accuracy suggest the viability of incorporating AI support for improved diagnostic outcomes in non-specialized medical centers.
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Metadata
Title
Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds
Authors
D. Carter
D. Bykhovsky
A. Hasky
I. Mamistvalov
Y. Zimmer
E. Ram
O. Hoffer
Publication date
01-12-2024
Publisher
Springer International Publishing
Published in
Techniques in Coloproctology / Issue 1/2024
Print ISSN: 1123-6337
Electronic ISSN: 1128-045X
DOI
https://doi.org/10.1007/s10151-024-02917-3

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