Hypertension Detection Using AI and Retinal Image Analysis

Srinivasan Balapangu Shankar; Margaret Richardson Ansah; Robert Ameyaw; Akyereba Kukua Sam; John Jeff Abbu-Bonsra Kyei; Deborah Amo; Percy Okae

doi:10.1109/ICAST61769.2024.10856490

Hypertension Detection Using AI and Retinal Image Analysis

Srinivasan Balapangu Shankar
, Margaret Richardson Ansah
, Robert Ameyaw
, Akyereba Kukua Sam
, John Jeff Abbu-Bonsra Kyei
, Deborah Amo
, Percy Okae

University of Ghana

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

This research paper introduces a method for detecting hypertensive retinopathy using advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs) applied to retinal fundus images. By analysing a set of over 5469 images from the UK Biobank we were able to achieve an accuracy rate of 81% in identifying signs of hypertensive retinopathy. Additionally, we improved our model by incorporating the detection of multiple eye conditions such, as diabetes, glaucoma, cataracts and age-related macular degeneration leading to overall diagnostic precision and providing a comprehensive screening tool. To make it easier for healthcare professionals to use this model in practice we developed a user-friendly mobile application that allows them to upload retinal images and receive diagnostic predictions displayed in visual formats like histograms and bar charts. This innovation not only makes the model accessible but also provides intuitive visual feedback, aiding in clinical decision-making.

Original language	English
Title of host publication	Proceedings of the 2024 IEEE 9th International Conference on Adaptive Science and Technology, ICAST 2024
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350385403
DOIs	https://doi.org/10.1109/ICAST61769.2024.10856490
Publication status	Published - 2024
Event	9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024 - Accra Duration: 24 Oct 2024 → 26 Oct 2024

Publication series

Name	IEEE International Conference on Adaptive Science and Technology, ICAST
ISSN (Print)	2326-9413
ISSN (Electronic)	2326-9448

Conference

Conference	9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024
Country/Territory	Ghana
City	Accra
Period	24/10/24 → 26/10/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Artificial Intelligence
Convolutional Neural Networks
Hypertensive Retinopathy
Mobile Application
Multi- Disease Detection
Retinal Fundus Images

Access to Document

10.1109/ICAST61769.2024.10856490

Cite this

Shankar, S. B., Richardson Ansah, M., Ameyaw, R., Sam, A. K., Jeff Abbu-Bonsra Kyei, J., Amo, D., & Okae, P. (2024). Hypertension Detection Using AI and Retinal Image Analysis. In Proceedings of the 2024 IEEE 9th International Conference on Adaptive Science and Technology, ICAST 2024 (IEEE International Conference on Adaptive Science and Technology, ICAST). IEEE Computer Society. https://doi.org/10.1109/ICAST61769.2024.10856490

@inproceedings{6ad7e5930dbe41a3bb2b0a1a1a413c88,

title = "Hypertension Detection Using AI and Retinal Image Analysis",

abstract = "This research paper introduces a method for detecting hypertensive retinopathy using advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs) applied to retinal fundus images. By analysing a set of over 5469 images from the UK Biobank we were able to achieve an accuracy rate of 81\% in identifying signs of hypertensive retinopathy. Additionally, we improved our model by incorporating the detection of multiple eye conditions such, as diabetes, glaucoma, cataracts and age-related macular degeneration leading to overall diagnostic precision and providing a comprehensive screening tool. To make it easier for healthcare professionals to use this model in practice we developed a user-friendly mobile application that allows them to upload retinal images and receive diagnostic predictions displayed in visual formats like histograms and bar charts. This innovation not only makes the model accessible but also provides intuitive visual feedback, aiding in clinical decision-making.",

keywords = "Artificial Intelligence, Convolutional Neural Networks, Hypertensive Retinopathy, Mobile Application, Multi- Disease Detection, Retinal Fundus Images",

author = "Shankar, \{Srinivasan Balapangu\} and \{Richardson Ansah\}, Margaret and Robert Ameyaw and Sam, \{Akyereba Kukua\} and \{Jeff Abbu-Bonsra Kyei\}, John and Deborah Amo and Percy Okae",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024 ; Conference date: 24-10-2024 Through 26-10-2024",

year = "2024",

doi = "10.1109/ICAST61769.2024.10856490",

language = "English",

series = "IEEE International Conference on Adaptive Science and Technology, ICAST",

publisher = "IEEE Computer Society",

booktitle = "Proceedings of the 2024 IEEE 9th International Conference on Adaptive Science and Technology, ICAST 2024",

}

Shankar, SB, Richardson Ansah, M, Ameyaw, R, Sam, AK, Jeff Abbu-Bonsra Kyei, J, Amo, D & Okae, P 2024, Hypertension Detection Using AI and Retinal Image Analysis. in Proceedings of the 2024 IEEE 9th International Conference on Adaptive Science and Technology, ICAST 2024. IEEE International Conference on Adaptive Science and Technology, ICAST, IEEE Computer Society, 9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024, Accra, Ghana, 24/10/24. https://doi.org/10.1109/ICAST61769.2024.10856490

Hypertension Detection Using AI and Retinal Image Analysis. / Shankar, Srinivasan Balapangu; Richardson Ansah, Margaret; Ameyaw, Robert et al.
Proceedings of the 2024 IEEE 9th International Conference on Adaptive Science and Technology, ICAST 2024. IEEE Computer Society, 2024. (IEEE International Conference on Adaptive Science and Technology, ICAST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hypertension Detection Using AI and Retinal Image Analysis

AU - Shankar, Srinivasan Balapangu

AU - Richardson Ansah, Margaret

AU - Ameyaw, Robert

AU - Sam, Akyereba Kukua

AU - Jeff Abbu-Bonsra Kyei, John

AU - Amo, Deborah

AU - Okae, Percy

PY - 2024

Y1 - 2024

N2 - This research paper introduces a method for detecting hypertensive retinopathy using advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs) applied to retinal fundus images. By analysing a set of over 5469 images from the UK Biobank we were able to achieve an accuracy rate of 81% in identifying signs of hypertensive retinopathy. Additionally, we improved our model by incorporating the detection of multiple eye conditions such, as diabetes, glaucoma, cataracts and age-related macular degeneration leading to overall diagnostic precision and providing a comprehensive screening tool. To make it easier for healthcare professionals to use this model in practice we developed a user-friendly mobile application that allows them to upload retinal images and receive diagnostic predictions displayed in visual formats like histograms and bar charts. This innovation not only makes the model accessible but also provides intuitive visual feedback, aiding in clinical decision-making.

AB - This research paper introduces a method for detecting hypertensive retinopathy using advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs) applied to retinal fundus images. By analysing a set of over 5469 images from the UK Biobank we were able to achieve an accuracy rate of 81% in identifying signs of hypertensive retinopathy. Additionally, we improved our model by incorporating the detection of multiple eye conditions such, as diabetes, glaucoma, cataracts and age-related macular degeneration leading to overall diagnostic precision and providing a comprehensive screening tool. To make it easier for healthcare professionals to use this model in practice we developed a user-friendly mobile application that allows them to upload retinal images and receive diagnostic predictions displayed in visual formats like histograms and bar charts. This innovation not only makes the model accessible but also provides intuitive visual feedback, aiding in clinical decision-making.

KW - Artificial Intelligence

KW - Convolutional Neural Networks

KW - Hypertensive Retinopathy

KW - Mobile Application

KW - Multi- Disease Detection

KW - Retinal Fundus Images

UR - https://www.scopus.com/pages/publications/85217846285

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DO - 10.1109/ICAST61769.2024.10856490

M3 - Conference contribution

AN - SCOPUS:85217846285

T3 - IEEE International Conference on Adaptive Science and Technology, ICAST

BT - Proceedings of the 2024 IEEE 9th International Conference on Adaptive Science and Technology, ICAST 2024

PB - IEEE Computer Society

T2 - 9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024

Y2 - 24 October 2024 through 26 October 2024

ER -

Shankar SB, Richardson Ansah M, Ameyaw R, Sam AK, Jeff Abbu-Bonsra Kyei J, Amo D et al. Hypertension Detection Using AI and Retinal Image Analysis. In Proceedings of the 2024 IEEE 9th International Conference on Adaptive Science and Technology, ICAST 2024. IEEE Computer Society. 2024. (IEEE International Conference on Adaptive Science and Technology, ICAST). doi: 10.1109/ICAST61769.2024.10856490

Hypertension Detection Using AI and Retinal Image Analysis

Abstract

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UN SDGs

Keywords

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