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OPTICAL COHERENCE TOMOGRAPHY BIOMARKERS TO DISTINGUISH DIABETIC MACULAR EDEMA FROM PSEUDOPHAKIC CYSTOID MACULAR EDEMA USING MACHINE LEARNING ALGORITHMS

Hecht, Idan, BSc*; Bar, Asaf, MD*; Rokach, Lior, PhD; Noy Achiron, Romi, BSc*; Munk, Marion R., MD, PhD‡,§,¶; Huf, Wolfgang, MD**; Burgansky-Eliash, Zvia, MD*; Achiron, Asaf, MD*

doi: 10.1097/IAE.0000000000002342
Original Study: PDF Only

Purpose: In diabetic patients presenting with macular edema (ME) shortly after cataract surgery, identifying the underlying pathology can be challenging and influence management. Our aim was to develop a simple clinical classifier able to confirm a diabetic etiology using few spectral domain optical coherence tomography parameters.

Methods: We analyzed spectral domain optical coherence tomography data of 153 patients with either pseudophakic cystoid ME (n = 57), diabetic ME (n = 86), or “mixed” (n = 10). We used advanced machine learning algorithms to develop a predictive classifier using the smallest number of parameters.

Results: Most differentiating were the existence of hard exudates, hyperreflective foci, subretinal fluid, ME pattern, and the location of cysts within retinal layers. Using only 3 to 6 spectral domain optical coherence tomography parameters, we achieved a sensitivity of 94% to 98%, specificity of 94% to 95%, and an area under the curve of 0.937 to 0.987 (depending on the method) for confirming a diabetic etiology. A simple decision flowchart achieved a sensitivity of 96%, a specificity of 95%, and an area under the curve of 0.937.

Conclusion: Confirming a diabetic etiology for edema in cases with uncertainty between diabetic cystoid ME and pseudophakic ME was possible using few spectral domain optical coherence tomography parameters with high accuracy. We propose a clinical decision flowchart for cases with uncertainty, which may support the decision for intravitreal injections rather than topical treatment.

In diabetic patients presenting with macular edema after cataract surgery, discerning between diabetic macular edema and pseudophakic cystoid macular edema can be challenging and influence management. Using advanced machine learning algorithms, we developed a spectral domain optical coherence tomography clinical decision flowchart for cases with uncertainty, which may support the decision for intravitreal injections rather than topical treatment.

*Department of Ophthalmology, Edith Wolfson Medical Center and Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel;

Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel;

Department of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland;

§Bern Photographic Reading Center, University of Bern, Bern, Switzerland;

Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; and

**Vienna Hospital Association, Karl Landsteiner Institute for Clinical Risk Management, Vienna, Austria.

Reprint requests: Idan Hecht, BSc, Department of Ophthalmology, Edith Wolfson Medical Center, 62 Halochamim Street, Holon 58100, Israel; e-mail: Idanhe@gmail.com

Financial disclosures (M.R.M.): Lecturer fees: Novartis and Zeiss; Consultancy fees: Novartis, Lumithera, and Zeiss. Travel support: Bayer.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.retinajournal.com).

© 2018 by Ophthalmic Communications Society, Inc.