{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T01:01:41Z","timestamp":1790211701133,"version":"4.1.0"},"reference-count":36,"publisher":"SPIE-Intl Soc Optical Eng","issue":"01","license":[{"start":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T00:00:00Z","timestamp":1708560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Med. Imag."],"published-print":{"date-parts":[[2024,2,22]]},"abstract":"<jats:p>PurposeAccurate whole-gland prostate segmentation is crucial for successful ultrasound-MRI fusion biopsy, focal cancer treatment, and radiation therapy techniques. Commercially available artificial intelligence (AI) models, using deep learning algorithms (DLAs) for prostate gland segmentation, are rapidly increasing in numbers. Typically, their performance in a true clinical context is scarcely examined or published. We used a heterogenous clinical MRI dataset in this study aiming to contribute to validation of AI-models.ApproachWe included 123 patients in this retrospective multicenter (7 hospitals), multiscanner (8 scanners, 2 vendors, 1.5T and 3T) study comparing prostate contour assessment by 2 commercially available Food and Drug Association (FDA)-cleared and CE-marked algorithms (DLA1 and DLA2) using an expert radiologist\u2019s manual contours as a reference standard (RSexp) in this clinical heterogeneous MRI dataset. No in-house training of the DLAs was performed before testing. Several methods for comparing segmentation overlap were used, the Dice similarity coefficient (DSC) being the most important.ResultsThe DSC mean and standard deviation for DLA1 versus the radiologist reference standard (RSexp) was 0.90\u00b10.05 and for DLA2 versus RSexp it was 0.89\u00b10.04. A paired t-test to compare the DSC for DLA1 and DLA2 showed no statistically significant difference (p=0.8).ConclusionsTwo commercially available DL algorithms (FDA-cleared and CE-marked) can perform accurate whole-gland prostate segmentation on a par with expert radiologist manual planimetry on a real-world clinical dataset. Implementing AI models in the clinical routine may free up time that can be better invested in complex work tasks, adding more patient value.<\/jats:p>","DOI":"10.1117\/1.jmi.11.1.015002","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T04:39:31Z","timestamp":1708576771000},"page":"1-10","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning performance on MRI prostate gland segmentation: evaluation of two commercially available algorithms compared with an expert radiologist"],"prefix":"10.1117","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4663-8520","authenticated-orcid":false,"given":"Erik","family":"Thimansson","sequence":"first","affiliation":[{"name":"Lund University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik","family":"Baubeta","sequence":"additional","affiliation":[{"name":"Lund 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