Key result
KNN outperforms other machine learning models in detecting ZO-1 damage in corneal endothelial cells.
Why the study?
The corneal endothelium maintains clarity through tight junctions primarily constituted by ZO-1, and the study aimed to automate the detection of ZO-1 damage using machine learning techniques.
Do specific machine learning algorithms accurately detect ZO-1 damage in corneal endothelial cells?
Do specific machine learning algorithms accurately detect ZO-1 damage in corneal endothelial cells?
The KNN algorithm is the most effective classifier among those tested for detecting ZO-1 damage in corneal endothelial cells, potentially aiding medical professionals in assessing corneal health.
May aid corneal endothelial assessment; leaves open clinical adoption pending prospective validation.
The cornea is a critical component of the human eye, essential for forming clear images on the retina. It comprises multiple layers, each contributing to the transparency and health of the tissue. The corneal endothelium, a key structure within the cornea, plays an important role to maintain clarity through its tight junctions, primarily constituted by Zonula Occludens-1 (ZO-1). It is a cytoplasmic protein located in the outer layers of endothelial cells and is crucial for the integrity of these tight junctions. This work aims to detect damage to ZO-1 in endothelial cells by applying Gabor filters to ZO-1 images, followed by feature extraction and the application of various machine learning techniques. The machine learning algorithms used include Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Naïve Bayes, and Stochastic Gradient Descent (SGD). Our findings show that the KNN algorithm outperforms the others, achieving the highest accuracy in identifying ZO-1 damage, while SGD exhibited the lowest performance. Consequently, KNN appears to be the most effective classifier for detecting ZO-1 damage, offering valuable support for doctors and medical professionals in making quick, accurate decisions regarding corneal endothelial health.
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Doke et al. (2024) studied Corneal endothelium tight junction damage. Machine learning algorithms (KNN, SVM, Decision Tree, Naïve Bayes, SGD) with Gabor filters vs. Comparison among different ML algorithms was evaluated on Accuracy in identifying ZO-1 damage. The K-Nearest Neighbors (KNN) algorithm achieved the highest accuracy in detecting ZO-1 damage in corneal endothelial cells compared to other machine learning models.
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