TY - JOUR
T1 - Optical coherence tomography biomarkers to distinguish diabetic macular edema from pseudophakic cystoid macular edema using machine learning algorithms
AU - Hecht, Idan
AU - Bar, Asaf
AU - Rokach, Lior
AU - Achiron, Romi Noy
AU - Munk, Marion R.
AU - Munk, Marion R.
AU - Munk, Marion R.
AU - Huf, Wolfgang
AU - Burgansky-Eliash, Zvia
AU - Achiron, Asaf
N1 - Publisher Copyright:
Copyright © 2020 by Ophthalmic Communications Society, Inc.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Diabetic macular edema
KW - Optical coherence tomography
KW - Pseudophakic cystoid macular edema
UR - http://www.scopus.com/inward/record.url?scp=85075536523&partnerID=8YFLogxK
U2 - 10.1097/iae.0000000000002342
DO - 10.1097/iae.0000000000002342
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C2 - 30312254
AN - SCOPUS:85075536523
SN - 0275-004X
VL - 39
SP - 2283
EP - 2291
JO - Retina
JF - Retina
IS - 12
ER -