AI applications in routine clinical imaging: detection, segmentation, diagnosis, and prognosis

Akshayaa Vaidyanathan

Research output: ThesisDoctoral ThesisExternal prepared

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Abstract

In this dissertation, applicability of Artificial intelligence (AI) in the medical imaging field has been investigated on respiratory, oncological and otolaryngology use-cases. AI-based model centric approaches were explored and validated to prove generalizability when compared to expert radiologists and clinicians. Methods to prove explainability of the AI models in the language of clinicians were also investigated. Overall, the thesis proves the overall hypothesis that (semi) automated AI based methodologies can produce generalizable performance equivalent to that of an expert human charged with the same tasks and is exemplified in detection, diagnosis, and treatment response prediction use cases. The (semi) AI based methodologies were explored specifically on the following clinical problem statements: 1) Covid-19 diagnosis and differential diagnosis using CT imaging, 2) Pulmonary embolism detection and diagnosis using CTPA imaging, 3) Treatment response prediction in patients with non-metastatic non-small cell lung cancer using CT imaging and 4) Menière’s disease diagnosis using MRI imaging.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Maastricht University
Supervisors/Advisors
  • Lambin, Philippe, Supervisor
  • Woodruff, Henry, Co-Supervisor
  • Walsh, S., Co-Supervisor, External person
Award date28 Feb 2023
Place of PublicationMaastricht
Publisher
Print ISBNs9789464691955
DOIs
Publication statusPublished - 2023

Keywords

  • artificial intelligence
  • radiomics
  • precision diagnosis
  • clinical explainable AI (clinical x-AI)

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