Defining functional requirements for a patient-centric computerized glaucoma treatment and care ecosystem

Goldmann, Nahum; Skalicky, Simon E.; Weinreb, Robert N.; Guedes, Ricardo Augusto Paletta; Baudouin, Christophe; Zhang, Xiulan; Gestel, Aukje van; Blumenthal, Eytan Z.; Kaufman, Paul L.; Rothman, Robert; Vasquez, Ana Maria; Harasymowycz, Paul; Welsbie, Derek S.; Goldberg, Ivan

doi:10.21037/jmai-22-33

Cited by 2 publications

(1 citation statement)

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“…Furthermore, the evaluation of glaucoma progression requires the analysis of time-series and multimodal data of patients, including IOP measurements, fundus photography, OCT, and VF sensitivity. Therefore, various strategies are required to develop AI for diagnosing, monitoring, and treating glaucoma, and future prospective validation studies are crucial to evaluating the accuracy and reliability of glaucoma screening AI models in real-world settings [71].…”

Section: Discussionmentioning

confidence: 99%

Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device

Bekollari,

Dettoraki,

Stavrou

et al. 2024

Diagnostics

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Glaucoma is a chronic, progressive eye disease affecting the optic nerve, which may cause visual damage and blindness. In this study, we present a machine-learning investigation to classify patients with glaucoma (case group) with respect to normal participants (control group). We examined 172 eyes at the Ophthalmology Clinic of the “Elpis” General Hospital of Athens between October 2022 and September 2023. In addition, we investigated the glaucoma classification in terms of the following: (a) eye selection and (b) gender. Our methodology was based on the features extracted via two diagnostic optical systems: (i) conventional optical coherence tomography (OCT) and (ii) a modern RETeval portable device. The machine-learning approach comprised three different classifiers: the Bayesian, the Probabilistic Neural Network (PNN), and Support Vectors Machines (SVMs). For all cases examined, classification accuracy was found to be significantly higher when using the RETeval device with respect to the OCT system, as follows: 14.7% for all participants, 13.4% and 29.3% for eye selection (right and left, respectively), and 25.6% and 22.6% for gender (male and female, respectively). The most efficient classifier was found to be the SVM compared to the PNN and Bayesian classifiers. In summary, all aforementioned comparisons demonstrate that the RETeval device has the advantage over the OCT system for the classification of glaucoma patients by using the machine-learning approach.

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Section: Discussionmentioning

confidence: 99%