Deep learning model using retinal vascular images for classifying schizophrenia

Appaji, Abhishek; Harish, Vaishak; Korann, Vittal; Devi, Priyanka; Jacob, Arpitha; Padmanabha, Anantha; Kumar, Vijay; Varambally, Shivarama; Venkatasubramanian, Ganesan; Rao, Shyam Vasudeva; Suma, H. N.; Webers, Caroll; Berendschot, Tos T. J. M.; Rao, Naren P.

doi:10.1016/j.schres.2022.01.058

Cited by 16 publications

(8 citation statements)

References 47 publications

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“…Most of the studies were based on MRI; in the latter, EEG studies predominated. In the first group, one study was based on the analysis of fundal retinal images (Appaji et al 2022). This modality allowed direct visualization of blood vessels (and microvasculature) supplying the brain.…”

Section: Resultsmentioning

confidence: 99%

Automated detection of schizophrenia using deep learning: a review for the last decade

et al. 2023

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Schizophrenia (SZ) is a devastating mental disorder that disrupts higher brain functions like thought, perception, etc., with a profound impact on the individual’s life. Deep learning (DL) can detect SZ automatically by learning signal data characteristics hierarchically without the need for feature engineering associated with traditional machine learning. We performed a systematic review of DL models for SZ detection. Various deep models like long short-term memory, convolution neural networks, AlexNet, etc., and composite methods have been published based on electroencephalographic signals, and structural and/or functional magnetic resonance imaging acquired from SZ patients and healthy patients control subjects in diverse public and private datasets. The studies, the study datasets, model methodologies, and quantitative and statistical comparison of results obtained by the studies are reported in detail. . In addition, the challenges of DL models for SZ diagnosis and future works are discussed.

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Section: Resultsmentioning

confidence: 99%

Automated detection of schizophrenia using deep learning: a review for the last decade

et al. 2023

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“…With the advances of artificial intelligence and merging eye-tracking technology [ 62 ], ophthalmic photography has been increasingly used for the prediction of psychiatric disorders such as schizophrenia (SCZ) [ 36 , 63 ] and autism spectrum disorders [ 64 , 65 ]. For example, a deep learning algorithm developed by Appaji et al [ 36 ] achieved an AUC of 0.98 for classifying SCZ. This result suggests the great potential utility of fundus images in the diagnosis of psychiatric disorders as these protocols improve.…”

Section: Resultsmentioning

confidence: 99%

“… Classification of the included articles according to the image modalities used and the target systemic diseases [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. …”

Section: Figurementioning

confidence: 99%

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

Cao

Grzybowski³

et al. 2023

Healthcare

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The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.

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“…Binary classification of schizophrenia spectrum disorder (SSD) patients and healthy controls was also presented in 15 , where authors used classical classification models, especially logistic regression. The latest Indian research 16 , which included probably the largest group of patients so far, seems to be promising considering the results of analyzes using a trained convolution neural network deep learning algorithm, but this study evaluated retinal vascular abnormalities based on fundus camera images, not OCT data.…”

Section: Introductionmentioning

confidence: 99%

On the Improvement of Schizophrenia Detection with Optical Coherence Tomography Data Using Deep Neural Networks and Aggregation Functions

Karczmarek,

Plechawska-Wójcik,

Kiersztyn

et al. 2024

Preprint

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Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defined psychopathological picture. Despite many efforts, a definitive disease biomarker has still not been identified. One of the promising candidate for such biomarker might be the retinal morphology. In this study Optical Coherence Tomography (OCT) data is applied to assess the different layers of the retina. OCT data were applied in the process of automatic differentiation of schizophrenic patients and healthy controls. Numerical experiments covered applying several individual 1D Convolutional Neural Network based models as well as further using the aggregation of classification results to improve the initial classification results. The main goal of the study was to check how methods based on the aggregation of classification results work in classifying neuroanatomical features of schizophrenia. Among over 300, 000 different variants of tested aggregation operators, a few versions provided satisfactory results.

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Deep learning model using retinal vascular images for classifying schizophrenia

Cited by 16 publications

References 47 publications

Automated detection of schizophrenia using deep learning: a review for the last decade

Automated detection of schizophrenia using deep learning: a review for the last decade

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

On the Improvement of Schizophrenia Detection with Optical Coherence Tomography Data Using Deep Neural Networks and Aggregation Functions

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