Artificial intelligence for medical image interpretation using expert knowledge and machine learning

Lars Erik Olof Jacobson; Adrian Alan Hopgood; Mohamed Bader-El-Den; Vincenzo Tamma; David Prendergast; Peter Osborn; Shah Sufi Nesar Uddin Siddiqui; Alexander Gegov; Farzad Arabikhan

doi:10.1109/CAI54212.2023.00059

Artificial intelligence for medical image interpretation using expert knowledge and machine learning

Lars Erik Olof Jacobson, Adrian Alan Hopgood, Mohamed Bader-El-Den, Vincenzo Tamma, David Prendergast, Peter Osborn, Shah Sufi Nesar Uddin Siddiqui, Alexander Gegov, Farzad Arabikhan

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

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Abstract

In 2022 268,490 new cases and 34,500 deaths was estimated for prostate cancer in the United States. Diagnosis of prostate cancer is primarily based on prostate-specific anti- gen (PSA) screening and trans-rectal ultrasound (TRUS)-guided prostate biopsy. PSA has a low specificity of 36% since benign conditions can elevate the PSA levels. The data set used for prostate cancer consists of t2-weighted MR images for 1,151 patients and 61,119 images. This paper presents an approach to applying knowledge-based artificial intelligence together with image segmentation to improve the diagnosis of prostate cancer using publicly available data. Complete and reliable segmentation into the transition zone and peripheral zone is required in order to automate and enhance the process of prostate cancer diagnosis.

Original language	English
Title of host publication	Proceedings of the 2023 IEEE Conference on Artificial Intelligence
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	122-123
Number of pages	2
ISBN (Electronic)	9798350339840
ISBN (Print)	9798350339857
DOIs	https://doi.org/10.1109/CAI54212.2023.00059
Publication status	Published - 2 Aug 2023
Event	2023 IEEE Conference on Artificial Intelligence - Hyatt Regency Santa Clara 5101 Great America Parkway, Santa Clara, United States Duration: 5 Jun 2023 → 6 Jun 2023 https://cai.ieee.org/2023/

Conference

Conference	2023 IEEE Conference on Artificial Intelligence
Abbreviated title	IEEE CAI
Country/Territory	United States
City	Santa Clara
Period	5/06/23 → 6/06/23
Internet address	https://cai.ieee.org/2023/

Keywords

image segmentation
prostate
magnetic resonance imaging
hybrid systems

UN SDGs

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

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10.1109/CAI54212.2023.00059

2023126193
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Accepted author manuscript (Post-print), 1.02 MB

https://cai.ieee.org/2023

Cite this

Jacobson, L. E. O., Hopgood, A. A., Bader-El-Den, M., Tamma, V., Prendergast, D., Osborn, P., Siddiqui, S. S. N. U., Gegov, A., & Arabikhan, F. (2023). Artificial intelligence for medical image interpretation using expert knowledge and machine learning. In Proceedings of the 2023 IEEE Conference on Artificial Intelligence (pp. 122-123). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAI54212.2023.00059

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abstract = "In 2022 268,490 new cases and 34,500 deaths was estimated for prostate cancer in the United States. Diagnosis of prostate cancer is primarily based on prostate-specific anti- gen (PSA) screening and trans-rectal ultrasound (TRUS)-guided prostate biopsy. PSA has a low specificity of 36% since benign conditions can elevate the PSA levels. The data set used for prostate cancer consists of t2-weighted MR images for 1,151 patients and 61,119 images. This paper presents an approach to applying knowledge-based artificial intelligence together with image segmentation to improve the diagnosis of prostate cancer using publicly available data. Complete and reliable segmentation into the transition zone and peripheral zone is required in order to automate and enhance the process of prostate cancer diagnosis.",

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author = "Jacobson, {Lars Erik Olof} and Hopgood, {Adrian Alan} and Mohamed Bader-El-Den and Vincenzo Tamma and David Prendergast and Peter Osborn and Siddiqui, {Shah Sufi Nesar Uddin} and Alexander Gegov and Farzad Arabikhan",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Conference<br/>on Artificial Intelligence, IEEE CAI ; Conference date: 05-06-2023 Through 06-06-2023",

year = "2023",

month = aug,

day = "2",

doi = "10.1109/CAI54212.2023.00059",

language = "English",

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Jacobson, LEO , Hopgood, AA , Bader-El-Den, M , Tamma, V, Prendergast, D, Osborn, P, Siddiqui, SSNU, Gegov, A & Arabikhan, F 2023, Artificial intelligence for medical image interpretation using expert knowledge and machine learning. in Proceedings of the 2023 IEEE Conference on Artificial Intelligence. Institute of Electrical and Electronics Engineers Inc., pp. 122-123, 2023 IEEE Conference
on Artificial Intelligence, Santa Clara, California, United States, 5/06/23. https://doi.org/10.1109/CAI54212.2023.00059

Artificial intelligence for medical image interpretation using expert knowledge and machine learning. / Jacobson, Lars Erik Olof ; Hopgood, Adrian Alan ; Bader-El-Den, Mohamed et al.
Proceedings of the 2023 IEEE Conference on Artificial Intelligence. Institute of Electrical and Electronics Engineers Inc., 2023. p. 122-123.

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

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T1 - Artificial intelligence for medical image interpretation using expert knowledge and machine learning

AU - Jacobson, Lars Erik Olof

AU - Hopgood, Adrian Alan

AU - Bader-El-Den, Mohamed

AU - Tamma, Vincenzo

AU - Prendergast, David

AU - Osborn, Peter

AU - Siddiqui, Shah Sufi Nesar Uddin

AU - Gegov, Alexander

AU - Arabikhan, Farzad

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AB - In 2022 268,490 new cases and 34,500 deaths was estimated for prostate cancer in the United States. Diagnosis of prostate cancer is primarily based on prostate-specific anti- gen (PSA) screening and trans-rectal ultrasound (TRUS)-guided prostate biopsy. PSA has a low specificity of 36% since benign conditions can elevate the PSA levels. The data set used for prostate cancer consists of t2-weighted MR images for 1,151 patients and 61,119 images. This paper presents an approach to applying knowledge-based artificial intelligence together with image segmentation to improve the diagnosis of prostate cancer using publicly available data. Complete and reliable segmentation into the transition zone and peripheral zone is required in order to automate and enhance the process of prostate cancer diagnosis.

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ER -

Artificial intelligence for medical image interpretation using expert knowledge and machine learning

Abstract

Conference

Keywords

UN SDGs

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