Deep Learning Using Images of the Retina for Assessment of Severity of Neurological Dysfunction in Parkinson Disease

Arslan, Janan; Racoceanu, Daniel; Benke, Kurt K.

doi:10.1001/jamaophthalmol.2022.6036

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Within this context, retinal imaging does not confer supplementary diagnostic insights compared to the existing gold standard. Conversely, its potential utility emerges in the early diagnostic phase, prior to the complete manifestation of all distinctive symptoms [81].…”

Section: Limitations and Considerationsmentioning

confidence: 99%

Parkinson's Disease: What Can Retinal Imaging Tell Us?

Di Pippo,

d’Agostino,

Ruggeri

et al. 2024

J. Integr. Neurosci.

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Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. While the diagnosis of PD primarily relies on clinical assessments and neurological examination, there has been growing interest in exploring non-invasive imaging techniques to aid in early detection and monitoring of the disease. In recent years, retinal imaging has emerged as a promising tool for studying PD due to the close anatomical and functional similarities between the retina and the brain. Retinal imaging methods, such as spectral domain optical coherence tomography and optical coherence tomography angiography, enable non-intrusive visualization and measurement of retinal structures and blood vessels. These techniques hold the promise of capturing alterations in retinal structure and function that could potentially mirror the underlying pathological mechanisms in PD. This review article aims to provide an overview of the current understanding of retinal changes in PD and the potential utility of retinal imaging as a diagnostic and monitoring tool.

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Section: Limitations and Considerationsmentioning

confidence: 99%

Parkinson's Disease: What Can Retinal Imaging Tell Us?

Di Pippo,

d’Agostino,

Ruggeri

et al. 2024

J. Integr. Neurosci.

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“…The input to the explainer is a DL classifier that mixes a pre-trained image classification network, Xception, 82 and a fully connected network. Arslan et al 83 x-rays, ECGs, and fundus photographs, literature concerning clinical infrared imaging is scarce. Vardhan and Krishna 84 introduced a Grad-CAM-based method to segment infrared breast images and identify areas of bias and weaknesses in the UNet architecture.…”

Section: Related Workmentioning

confidence: 99%

“…The input to the explainer is a DL classifier that mixes a pre‐trained image classification network, Xception, 82 and a fully connected network. Arslan et al 83 developed a DL model for assessing the severity of neurological dysfunction in Parkinson's disease using retina images. The study created a CNN‐based classifier using fundus photographs and patient demographic characteristics.…”

Section: Related Workmentioning

confidence: 99%

Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images

Raghavan,

Balasubramanian,

Veezhinathan

2024

Computational Intelligence

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There is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence‐based predictions and ensure transparency in decision‐making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision‐making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency‐based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non‐visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an “attention guided Grad‐CAM” that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community.

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Forecast the Onset of Parkinson’s Disease at all Three Stages using Deep Learning Techniques

El Fiorenza J,

Sellam,

Rose

2023

2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI)

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Deep Learning Using Images of the Retina for Assessment of Severity of Neurological Dysfunction in Parkinson Disease

Abstract: Cheung CY, Xu D, Cheng CY, et al. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre.

Cited by 4 publications

References 8 publications

Parkinson's Disease: What Can Retinal Imaging Tell Us?

Parkinson's Disease: What Can Retinal Imaging Tell Us?

Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images

Forecast the Onset of Parkinson’s Disease at all Three Stages using Deep Learning Techniques

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