Screening und Management retinaler Erkrankungen mittels digitaler Medizin

Gerendas, Bianca S.; Waldstein, Sebastian M.; Schmidt‐Erfurth, Ursula

doi:10.1007/s00347-018-0752-7

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Deep learning (DL) is a subset of artificial intelligence (AI) based on neural networks that use an active learning strategy in the automated detection of glaucoma based on fundus images [ 36 , 37 ]. It can recognize patterns with glaucomatous features in images quickly and accurately [ 8 ], achieving a robust performance in detecting other retinal pathologies such as diabetic retinopathy and retinopathy of prematurity, macular edema, and age-related macular degeneration [ 32 ], with the potential to assist specialists in mass screening of glaucoma [ 38 ], reducing costs, and offering the potential to solve complex problems involving large datasets with medical images and classify diseases with a good innovative perspective for the introduction of individualized medicine and the optimization of diagnosis and therapy, screening, and prognosis [ 22 , 39 , 40 ], with less dependence on the examiner’s experience [ 5 , 41 , 42 ], demonstrating the potential for implementation of large-scale screening protocols in the population, to screen for glaucomatous papilla in several evolutionary stages and monitor treatments [ 43 ].…”

Section: Computer Vision and Artificial Intelligencementioning

confidence: 99%

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

Camara,

Cunha

2024

Medicina

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Glaucoma is one of the leading causes of irreversible blindness in the world. Early diagnosis and treatment increase the chances of preserving vision. However, despite advances in techniques for the functional and structural assessment of the retina, specialists still encounter many challenges, in part due to the different presentations of the standard optic nerve head (ONH) in the population, the lack of explicit references that define the limits of glaucomatous optic neuropathy (GON), specialist experience, and the quality of patients’ responses to some ancillary exams. Computer vision uses deep learning (DL) methodologies, successfully applied to assist in the diagnosis and progression of GON, with the potential to provide objective references for classification, avoiding possible biases in experts’ decisions. To this end, studies have used color fundus photographs (CFPs), functional exams such as visual field (VF), and structural exams such as optical coherence tomography (OCT). However, it is still necessary to know the minimum limits of detection of GON characteristics performed through these methodologies. This study analyzes the use of deep learning (DL) methodologies in the various stages of glaucoma screening compared to the clinic to reduce the costs of GON assessment and the work carried out by specialists, to improve the speed of diagnosis, and to homogenize opinions. It concludes that the DL methodologies used in automated glaucoma screening can bring more robust results closer to reality.

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Section: Computer Vision and Artificial Intelligencementioning

confidence: 99%

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

Camara,

Cunha

2024

Medicina

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“…The results should help ophthalmologists to better define their therapy strategy for each patient in everyday practice. Moreover, Schmidt-Erfurth et al recently reported the potential of AI-based approaches for targeted optimization of diagnosis and therapy for eye diseases 7 . 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 .…”

Section: Introductionmentioning

confidence: 99%

Visual acuity prediction on real-life patient data using a machine learning based multistage system

Schlosser,

Beuth,

Meyer

et al. 2024

Sci Rep

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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.

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