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 proceedingConference 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 languageEnglish
Title of host publicationProceedings of the 2023 IEEE Conference on Artificial Intelligence
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages122-123
Number of pages2
ISBN (Electronic)9798350339840
ISBN (Print)9798350339857
DOIs
Publication statusPublished - 2 Aug 2023
Event2023 IEEE Conference
on Artificial Intelligence
- Hyatt Regency Santa Clara 5101 Great America Parkway, Santa Clara, United States
Duration: 5 Jun 20236 Jun 2023
https://cai.ieee.org/2023/

Conference

Conference2023 IEEE Conference
on Artificial Intelligence
Abbreviated titleIEEE CAI
Country/TerritoryUnited States
CitySanta Clara
Period5/06/236/06/23
Internet address

Keywords

  • image segmentation
  • prostate
  • magnetic resonance imaging
  • hybrid systems

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