iMIGS: An innovative AI based prediction system for selecting the best patient-specific glaucoma treatment

The use of AI-based techniques in healthcare are becoming more and more common and more disease-specific. Glaucoma is a disorder in eye that causes damage to the optic nerve which can lead to permanent blindness. It is caused by the elevated pressure inside the eye due to the obstruction to the flow of the drainage fluid (aqueous humor). Most recent treatment options involve minimally invasive glaucoma surgery (MIGS) in which a stent is placed to improve drainage of aqueous humor from the eye. Each MIGS surgery has a different mechanism of action, and the relative efficacy and chance of success is dependent on multiple patient-specific factors. Hence the ophthalmologists are faced with the critical question; which method would be better for a specific patient, both in terms of glaucoma control but also taking into consideration patient quality of life? In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed in the form of a Treatment Advice prediction system that will offer the clinician a suggested MIGS treatment from the baseline clinical parameters. ANFIS was used with a real-world MIGS data set which was a retrospective case series of 372 patients who underwent either of the four MIGS procedures from July 2016 till May 2020 at a single center in the UK. • Inputs used: Clinical measurements of Age, Visual Acuity, Intraocular Pressure (IOP), and Visual Field, etc. • Output Classes: iStent, iStent and Endoscopic Cyclophotocoagulation (ICE2), PreserFlo MicroShunt (PMS) and XEN-45). • Results: The proposed ANFIS system was found to be 91% accurate with high Sensitivity (80%) and Specificity (90%)

Adherence, underperformed and overperformed test rates – Novel metrics for reporting compliance to clinical guidelines

Background: Compliance to guidelines ensures evidence-based care, critical to optimal patient outcomes. There is currently no common and comprehensive method of reporting compliance to guidelines across multiple ocular conditions. Moreover, IP optometrist activity has yet to be evaluated in this manner. This paper presents novel metrics to promote the standardised reporting of compliance to clinical guidelines. Methods: Three novel metrics were developed; adherence (A), overperformed test rate (OP), and underperformed test rate (UP). These metrics were used to evaluate 822 first patient appointments collected over the course of a year (Nov 2018 - Oct 2019) by four specialist IP Optometrists (Acute Primary Care Ophthalmology Service, Kent). Compliance across 76 quality indicators (recommended tests) covering history and symptoms, clinical signs, management, and prescribing decisions was measured against the College of Optometrists’ Clinical Management Guidelines. Results: The metrics (mean and range) are as follows. History and symptoms, A: 78.9% (range: 48–98%), OP: 6.9% (range: 0-13.7%) and UP: 14.2% (range: 1.2-4.5%). Clinical signs (tests undertaken), A: 93.8% (61-99%), OP: 2.8% (range: 0-14%) and UP: 3.4% (range: 0-28%). Management decisions, A: 69.6% (range: 61-100%), OP: 3.1% (0-12%) and UP 27.3% (range: 0-57%). Prescribing decisions, A: 92.1% (range: 61-100%), MOP: 0.9% (range: 0-5%) and UP: 7.1% (range: 0-35%). Conclusion: The three novel metrics provide a comprehensive method of reporting compliance to guidelines. IP optometrist activity appears largely compliant against CMG recommendations across 48 anterior ocular conditions. Further work is being undertaken to explore the relationship between clinical observations and decisions made by IP optometrists

Translational Learning with Orange Data Mining

Abstract for the e-NATCONPH 2021 (International Conference) TRANSLATIONAL LEARNING WITH ORANGE DATA MINING Raqib F 1, Dunne MCM 1*, Gurney JC2, Harle DE 3, Sivapalan T 2, Sabokbar N 2, Bhogal-Bhamra GK 1 1 Ophthalmic Research Group, Optometry School, Aston University, Birmingham, UK 2 Acute Primary Care Ophthalmology Service, West Kent CCG, Aylesford, UK 3 Acute Primary Care Ophthalmology Service, West Kent CCG, Tonbridge, UK *Corresponding author’s email ID: [email protected] BACKGROUND. Health Education England’s Topol Review has recommended preparation of clinicians for a digital future. Orange Data Mining software enables hands-on exposure of machine learning to practitioners that traditionally lack this training. PURPOSE. This case study presents a translational learning approach, used for teaching undergraduate optometrists, that includes (a) gathering clinical evidence (b) learning from the clinical evidence and (c) translation to evidence-based teaching and practice. METHODOLOGY In this approach, students are taught about research ethics before creating an Orange Data Mining canvas containing widgets to upload clinical data (File), remove missing data (Impute), assign variables (Select columns), carry out machine learning (Naïve Bayes and Logistic Regression), master cross validation and hyperparameter tuning (Test and score) before gaining new knowledge and clinical decision support (Nomogram). This is demonstrated with 1351 real clinical cases for determining the relative importance of clinical data, recommended by the College of Optometrists’ Clinical Management Guidelines, for investigating an anterior eye disease (uveitis). RESULTS. Students discover that Naive Bayes has higher informedness (96%) than tuned Logistic Regression (90%). The Naïve Bayes nomogram reveals the relative importance of the clinical symptoms and signs while the Logistic regression nomogram indicates possible redundancy. A presentation of acute unilateral discomfort and visual disturbance with mild red eye and anterior chamber inflammation results in 90% and 68% probabilities of uveitis according, respectively, to Naïve Bayes’ and Logistic Regression nomograms. CONCLUSION. Our students enjoy this translational learning approach and we ask if it might also be useful for training other health scientists. Key Words: Education, Health Sciences, Translational learnin