Assessing the performance of AI-assisted technicians in liver segmentation, Couinaud division, and lesion detection: a pilot study
With AI assistance, non-radiologist experienced operators showed good agreement with radiologists for quantifying whole liver volume, Couinaud segment volume, and the detection and measurement of lesions in patients with known liver cancer. This AI-assisted non-radiologist approach has potential to reduce the stress on radiologists without compromising accuracy.
Authors
- Mohammad Ali Husainy (ORCID: https://orcid.org/0000-0003-1907-175X)
- Andrea Dennis (ORCID: https://orcid.org/0000-0002-0152-6528)
- Carlos Ferreira
- Luis Núñez (ORCID: https://orcid.org/0000-0001-8403-9380)
- Stefano Cappio
- Hildo J. Lamb (ORCID: https://orcid.org/0000-0002-6969-2418)
- Michele Pansini (ORCID: https://orcid.org/0000-0002-5610-3266)
- Amirkasra Mojtahed (ORCID: https://orcid.org/0000-0003-1879-9255)
Institutions
- Leiden University (NL)
- Harvard University (US)
- Capgemini (Netherlands) (NL)
- Massachusetts General Hospital (US)
- Oxford University Hospitals NHS Trust (GB)
- Università della Svizzera italiana (CH)
Publication Details
- Journal
- Abdominal Radiology
- Published
- 2024-08-10
- DOI
- https://doi.org/10.1007/s00261-024-04507-1
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21