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Optical Coherence Tomography Radiomics and Machine Learning Enable Accurate Detection of Forme Fruste Keratoconus.

Shenglong Luo¹, Xuefei Li¹, Kuangching Lin¹

¹From the National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital (S.L., X.L., K.L., F.B., S.C., F.L., J.W.), Wenzhou Medical University, Wenzhou, Zhejiang Province, China; National Clinical Research Center for Ocular Diseases, Eye Hospital (S.L., X.L., K.L., F.B., S.C., F.L., J.W.), Wenzhou Medical University, Wenzhou, Zhejiang Province, China; State Key Laboratory of Eye Health, Eye Hospital (S.L., X.L., K.L., F.B., S.C., F.L., J.W.), Wenzhou Medical University, Wenzhou, Zhejiang Province, China.

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Summary

This summary is machine-generated.

Machine learning analysis of corneal optical coherence tomography (OCT) images accurately detects forme fruste keratoconus (FFKC). This radiomics approach offers a noninvasive method for early keratoconus screening, improving diagnostic capabilities.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Corneal imaging analysis is crucial for diagnosing eye conditions.
Early detection of keratoconus, such as forme fruste keratoconus (FFKC), is vital for effective management.
Conventional diagnostic methods may not always identify subtle early-stage changes.

Purpose of the Study:

To assess the diagnostic accuracy of a radiomics-based machine learning model using corneal optical coherence tomography (OCT) images for FFKC detection.
To evaluate the performance of different machine learning classifiers in identifying FFKC from OCT data.

Main Methods:

Corneal OCT images from 307 eyes (234 normal, 73 FFKC) were analyzed.
Texture-based radiomics features were extracted from the OCT images.
Three machine learning models (Random Forest, C5.0, XGBoost) were trained and evaluated using metrics like AUC, sensitivity, and specificity.

Main Results:

A radiomics approach extracted 3,752 features, with 41 selected for model training.
All tested machine learning models showed high diagnostic performance (AUC > 0.92).
The XGBoost model achieved the highest accuracy (AUC=0.93, specificity=0.978, accuracy=0.950), with key features concentrated in the inferotemporal cornea.

Conclusions:

Radiomics analysis of corneal OCT images combined with machine learning provides accurate FFKC detection.
This noninvasive, single-device approach offers diagnostic insights beyond standard morphological assessment.
The study suggests a potential imaging biomarker for early keratoconus screening and characterizing subclinical corneal ectasia.