Open Access Peer-reviewed Research Article

A Data-Driven Evaluation of ECD Measurement Techniques Across Traditional and AI-Based Modalities

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Comparison of learning and deep learning methods
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Submitted   Apr 14, 2025
Published   Jul 2, 2025
Issue    Vol 4 No 2 (2025)
DOI   10.25082/RIMA.2025.02.002

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Manghe Fidelis Obi
Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, Dayton, OH, USA
Andy Officer
Lions Eye Bank of West Central Ohio, Dayton, OH, USA
Shannon Schweitzer
Lions Eye Bank of West Central Ohio, Dayton, OH, USA
Tarun Goswami corresponding author
Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, Dayton, OH, USA

Abstract

Accurate measurement of corneal endothelial cell density (ECD) is crucial in evaluating the viability of donor corneas for transplantation. The consistency of ECD measurements is critical for predicting post-transplant results and monitoring corneal health. However, measurement methods have evolved, moving from manual counting to more complex semi-automatic and fully automated systems, including AI-powered solutions. This study compares the accuracy, dependability, and efficiency of manual, semi-automated, and fully automated ECD measurement techniques. It investigates the degree of heterogeneity among techniques and evaluates their potential to improve clinical outcomes in corneal transplantation. The sample includes corneal data from 300 participants, 150 male and 150 female donors, who were divided into three groups based on the measurement method: manual, semi-automated, or fully automated. The study also examined the gender distribution to see whether there was any difference in results between male and female donor corneas. Manual counting has previously been notable for its variability due to operator expertise and calibration discrepancies, with mean ECD values ranging from 2146 to 2775 cells/mm² (p < 0.05). Semi-automated procedures, which combine manual input with software aid, enhance consistency. In the Cornea Preservation Time Study, eye banks reported a mean ECD of 2773 ± 300 cells/mm², while CIARC reported 2758 ± 388 cells/mm², with agreement limits ranging from [-644, 675] cells/mm² (p < 0.05). The AxoNet deep learning model had a mean absolute error (MAE) of 12.1 cells/mm² and an R² value of 0.948, making it the most accurate fully automated system. A separate study on AI-based detection of aberrant endothelium cells achieved an accuracy of 0.95, precision of 0.92, recall of 0.94, and F1 score of 0.93, and an AUC-ROC of 0.98 (p < 0.01). Fully automated AI-based methods surpass manual and semi-automated approaches in accuracy and consistency, significantly reducing time and labor. The findings highlight the importance of adopting AI-driven technologies to enhance diagnostic precision and efficiency in clinical settings. However, the need for standardized calibration procedures and high- quality image acquisition remains critical for reliable ECD measurement.

Keywords
corneal endothelial cell density (ECD), manual cell counting, semi-automated cell counting, fully automated cell counting, deep learning, image analysis, AI in ophthalmology

Article Details

Supporting Agencies
The successful completion of this research was made possible through the collective efforts of individuals and institutions whose contributions are deeply appreciated. Special thanks are extended to the faculty and research staff whose guidance and expertise have provided invaluable insights into the methodologies explored in this study. Their critical feedback and support have played a significant role in refining the technical and analytical aspects of this work. Gratitude is also expressed to the research community whose previous studies and innovations in endothelial cell density measurement have served as the foundation for this work. The continuous advancements in machine learning, deep learning, and AI-driven automation have significantly shaped the direction and execution of this study. A special acknowledgment is extended to Dr. Darryl Ahner, Dean of the College of Engineering and Computer Science, whose financial support enabled the successful completion of this project. The resources provided have facilitated access to computational tools, datasets, and analytical frameworks essential for conducting this research. Finally, appreciation is given to colleagues, peers, and family members for their encouragement and support throughout the research process. Their motivation and feedback have contributed to the successful realization of this work.
How to Cite
Obi, M. F., Officer, A., Schweitzer, S., & Goswami, T. (2025). A Data-Driven Evaluation of ECD Measurement Techniques Across Traditional and AI-Based Modalities. Research on Intelligent Manufacturing and Assembly, 4(2), 239-265. https://doi.org/10.25082/RIMA.2025.02.002
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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