Which Explanatory Variables Contribute to the Classification of Good Visual Acuity over Time in Patients with Branch Retinal Vein Occlusion with Macular Edema Using Machine Learning?
Abstract:This study’s goal is to determine the accuracy of a linear classifier that predicts the prognosis of patients with macular edema (ME) due to a branch retinal vein occlusion during the maintenance phase of antivascular endothelial growth factor (anti-VEGF) therapy. The classifier was created using the clinical information and optical coherence tomographic (OCT) findings obtained up to the time of the first resolution of ME. In total, 66 eyes of 66 patients received an initial intravitreal injection of anti-VEGF… Show more
“…In their contributions, they furthermore describe the impact of deep learning (DL) for the prediction of patient progressions in the earlier stages of AMD utilizing OCT biomarkers 8 , 9 . Whereas current state-of-the-art research also explores the explainability as well as the related nomenclature when reporting AMD-related diseases 10 , 11 , AMD such as exudative AMD as well as DME and RVO can be seen as the three most prevalent investigated eye diseases within the context of AI 11 – 14 . Figure 1 Patient progression visualization and modelling dashboard developed with medical doctors for medical doctors as well as researchers.…”
In ophthalmology, intravitreal operative medication therapy (IVOM) is a widespread treatment for diseases related to the age-related macular degeneration (AMD), the diabetic macular edema, as well as the retinal vein occlusion. However, in real-world settings, patients often suffer from loss of vision on time scales of years despite therapy, whereas the prediction of the visual acuity (VA) and the earliest possible detection of deterioration under real-life conditions is challenging due to heterogeneous and incomplete data. In this contribution, we present a workflow for the development of a research-compatible data corpus fusing different IT systems of the department of ophthalmology of a German maximum care hospital. The extensive data corpus allows predictive statements of the expected progression of a patient and his or her VA in each of the three diseases. For the disease AMD, we found out a significant deterioration of the visual acuity over time. Within our proposed multistage system, we subsequently classify the VA progression into the three groups of therapy “winners”, “stabilizers”, and “losers” (WSL classification scheme). Our OCT biomarker classification using an ensemble of deep neural networks results in a classification accuracy (F1-score) of over 98%, enabling us to complete incomplete OCT documentations while allowing us to exploit them for a more precise VA modelling process. Our VA prediction requires at least four VA examinations and optionally OCT biomarkers from the same time period to predict the VA progression within a forecasted time frame, whereas our prediction is currently restricted to IVOM/no therapy. We achieve a final prediction accuracy of 69% in macro average F1-score, while being in the same range as the ophthalmologists with 57.8 and $$50 \pm 10.7$$
50
±
10.7
% F1-score.
“…In their contributions, they furthermore describe the impact of deep learning (DL) for the prediction of patient progressions in the earlier stages of AMD utilizing OCT biomarkers 8 , 9 . Whereas current state-of-the-art research also explores the explainability as well as the related nomenclature when reporting AMD-related diseases 10 , 11 , AMD such as exudative AMD as well as DME and RVO can be seen as the three most prevalent investigated eye diseases within the context of AI 11 – 14 . Figure 1 Patient progression visualization and modelling dashboard developed with medical doctors for medical doctors as well as researchers.…”
In ophthalmology, intravitreal operative medication therapy (IVOM) is a widespread treatment for diseases related to the age-related macular degeneration (AMD), the diabetic macular edema, as well as the retinal vein occlusion. However, in real-world settings, patients often suffer from loss of vision on time scales of years despite therapy, whereas the prediction of the visual acuity (VA) and the earliest possible detection of deterioration under real-life conditions is challenging due to heterogeneous and incomplete data. In this contribution, we present a workflow for the development of a research-compatible data corpus fusing different IT systems of the department of ophthalmology of a German maximum care hospital. The extensive data corpus allows predictive statements of the expected progression of a patient and his or her VA in each of the three diseases. For the disease AMD, we found out a significant deterioration of the visual acuity over time. Within our proposed multistage system, we subsequently classify the VA progression into the three groups of therapy “winners”, “stabilizers”, and “losers” (WSL classification scheme). Our OCT biomarker classification using an ensemble of deep neural networks results in a classification accuracy (F1-score) of over 98%, enabling us to complete incomplete OCT documentations while allowing us to exploit them for a more precise VA modelling process. Our VA prediction requires at least four VA examinations and optionally OCT biomarkers from the same time period to predict the VA progression within a forecasted time frame, whereas our prediction is currently restricted to IVOM/no therapy. We achieve a final prediction accuracy of 69% in macro average F1-score, while being in the same range as the ophthalmologists with 57.8 and $$50 \pm 10.7$$
50
±
10.7
% F1-score.
Purpose
To investigate the association between the time from onset to initial treatment and changes in visual acuity or the number of treatments in patients with branch retinal vein occlusion.
Methods
Thirty-nine eyes of 39 consecutive patients with untreated acute-phase BVO. The patients were initially treated with anti-vascular endothelial growth factor (VEGF) therapy and additional pro re nata therapy within six months of onset. The patients were classified according to the time from disease onset to the first treatment (group A: 28 days or less, group B: over 28 days).
Results
The mean age was 73 ± 8 years (53 to 88). The mean the first treatment time was 31.6 ± 17.9 days. The mean logMAR visual acuity at first treatment was 0.37 ± 0.30. The mean number of treatments was 3.1 ± 1.7. No significant association was observed between the time from onset to the first treatment and changes in the logMAR visual acuity. Patients in group A and central macular thickness at the initial visit were independently associated with the number of treatments at one year (p = 0.03 and p = 0.01, respectively).
Conclusions
At one year, the time between onset to the start of anti-VEGF therapy for BVO was not associated with subsequent visual acuity changes.
Objectives: To develop a machine learning logistic regression algorithm that can classify patients with an idiopathic macular hole (IMH) into those with good or poor vison at 6 months after a vitrectomy. In addition, to determine its accuracy and the contribution of the preoperative OCT characteristics to the algorithm. Methods: This was a single-center, cohort study. The classifier was developed using preoperative clinical information and the optical coherence tomographic (OCT) findings of 43 eyes of 43 patients who had undergone a vitrectomy. The explanatory variables were selected using a filtering method based on statistical significance and variance inflation factor (VIF) values, and the objective variable was the best-corrected visual acuity (BCVA) at 6 months postoperation. The discrimination threshold of the BCVA was the 0.15 logarithm of the minimum angle of the resolution (logMAR) units. Results: The performance of the classifier was 0.92 for accuracy, 0.73 for recall, 0.60 for precision, 0.74 for F-score, and 0.84 for the area under the curve (AUC). In logistic regression, the standard regression coefficients were 0.28 for preoperative BCVA, 0.13 for outer nuclear layer defect length (ONL_DL), −0.21 for outer plexiform layer defect length (OPL_DL) − (ONL_DL), and −0.17 for (OPL_DL)/(ONL_DL). In the IMH form, a stenosis pattern with a narrowing from the OPL to the ONL of the MH had a significant effect on the postoperative BCVA at 6 months. Conclusions: Our results indicate that (OPL_DL) − (ONL_DL) had a similar contribution to preoperative visual acuity in predicting the postoperative visual acuity. This model had a strong performance, suggesting that the preoperative visual acuity and MH characteristics in the OCT images were crucial in forecasting the postoperative visual acuity in IMH patients. Thus, it can be used to classify MH patients into groups with good or poor postoperative visual acuity, and the classification was comparable to that of previous studies using deep learning.
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