Journal article
OPTICAL COHERENCE TOMOGRAPHY BIOMARKERS TO DISTINGUISH DIABETIC MACULAR EDEMA FROM PSEUDOPHAKIC CYSTOID MACULAR EDEMA USING MACHINE LEARNING ALGORITHMS.
- Hecht I Department of Ophthalmology, Edith Wolfson Medical Center and Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel.
- Bar A Department of Ophthalmology, Edith Wolfson Medical Center and Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel.
- Rokach L Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
- Noy Achiron R Department of Ophthalmology, Edith Wolfson Medical Center and Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel.
- Munk MR Department of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
- Huf W Vienna Hospital Association, Karl Landsteiner Institute for Clinical Risk Management, Vienna, Austria.
- Burgansky-Eliash Z Department of Ophthalmology, Edith Wolfson Medical Center and Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel.
- Achiron A Department of Ophthalmology, Edith Wolfson Medical Center and Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel.
- 2018-10-13
Published in:
- Retina (Philadelphia, Pa.). - 2019
Aged
Area Under Curve
Biomarkers
Diabetic Retinopathy
Diagnosis, Computer-Assisted
Female
Fluorescein Angiography
Humans
Machine Learning
Macular Edema
Male
Middle Aged
Predictive Value of Tests
Pseudophakia
Reproducibility of Results
Retrospective Studies
Sensitivity and Specificity
Subretinal Fluid
Tomography, Optical Coherence
Visual Acuity
English
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 (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.
- Language
-
- English
- Open access status
- closed
- Identifiers
-
- DOI 10.1097/IAE.0000000000002342
- PMID 30312254
- Persistent URL
- https://sonar.ch/global/documents/46822
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