Development of "Predict ME, " an online classifier to aid in differentiating diabetic macular edema from pseudophakic macular edema. Issue 6 (November 2020)

• Record Type: Journal Article
• Authors: Hecht, Idan; Achiron, Ran; Bar, Asaf; Munk, Marion R; Huf, Wolfgang; Burgansky-Eliash, Zvia; Achiron, Asaf
• Is Part Of: European journal of ophthalmology. Volume 30:Issue 6(2020)
• Page Start: 1495
• Page End: 1498
• Publication Date: 2020-11
• Subjects: Diabetic macular edema -- pseudophakic cystoid macular edema -- optical coherence tomography -- machine learning -- automated classifier; Ophthalmology -- Periodicals; Eye -- Diseases -- Periodicals; 617.7005
• DOI: 10.1177/1120672119865355
• Languages: English
• ISSNs: 1120-6721

Abstract

Purpose: Differentiating the underlying pathology of macular edema in patients with diabetic retinopathy following cataract surgery can be challenging. In 2015, Munk and colleagues trained and tested a machine learning classifier which uses optical coherence tomography variables in order to distinguish the underlying pathology of macular edema between diabetic macular edema and pseudophakic cystoid macular edema. It was able to accurately diagnose the underlying pathology in 90%–96% of cases. However, actually using the trained classifier required dedicated software and advanced technical skills which hindered its accessibility to most clinicians. Our aim was to package the classifier in an easy to use web-tool and validate the web-tool using a new cohort of patients. Methods: We packaged the classifier in a web-tool intended for use on a personal computer or mobile phone. We first ensured that the results from the web-tool coincide exactly with the results from the original algorithm and then proceeded to test it using data of 14 patients. Results: The etiology was accurately predicted in 12 out of 14 cases (86%). The cases with diabetic macular edema were accurately diagnosed in 7 out of 7 cases. Of the pseudophakic cystoid macular edema cases, 5 out of 6 were correctly interpreted and 1 case with a mixed etiology was interpreted as pseudophakic cystoid macular edema. Variable input was reported to be easy and took on average 7 ± 3 min. Conclusion: The web-tool implementation of the classifier seems to be a valuable tool to support research into this field.