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Updated: Jul 1, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Comparing code-free and bespoke deep learning approaches in ophthalmology.

Carolyn Yu Tung Wong^1,2,3, Ciara O'Byrne^1,2, Priyal Taribagil^1,2

¹Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, UK.

Graefe'S Archive for Clinical and Experimental Ophthalmology = Albrecht Von Graefes Archiv Fur Klinische Und Experimentelle Ophthalmologie

|March 6, 2024

Summary

This summary is machine-generated.

Code-free deep learning (CFDL) enables clinicians to create AI models without coding. While promising for ophthalmology tasks, its advantages over expert-designed deep learning (DL) require context-specific evaluation.

Keywords:

Artificial intelligence Automated-machine learning Code-free deep learning Machine learning

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Informatics

Background:

Code-free deep learning (CFDL) empowers clinicians lacking coding skills to develop artificial intelligence (AI) models.
This review examines the advantages of CFDL compared to bespoke, expert-developed deep learning (DL) models.

Purpose of the Study:

To comprehensively review the benefits of CFDL over bespoke DL in ophthalmology.
To analyze CFDL applications in diabetic retinopathy screening, retinal multi-disease classification, surgical video classification, oculomics, and resource management.

Main Methods:

A systematic literature search was conducted in MEDLINE (PubMed) for studies on 'autoML' and 'ophthalmology' up to June 25, 2023.
Identified 5 CFDL studies and corresponding bespoke DL studies for comparative analysis, including ten relevant studies in total.

Main Results:

Studies generally favor CFDL over bespoke DL for the analyzed ophthalmological tasks.
Discussions on CFDL advantages often lack depth and applicability, necessitating context-specific assessments of clinician intent, patient acceptance, and cost-effectiveness.

Conclusions:

CFDL facilitates the prototyping of clinical AI systems for clinicians without deep learning expertise.
CFDL and bespoke DL models offer complementary strengths, with optimal selection depending on the specific task and requiring multidimensional evaluation.