Assessing the performance of AI-assisted technicians in liver segmentation, Couinaud division, and lesion detection: a pilot study
Rattachement africain : gb, us, nl, ch. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
BACKGROUND: In patients with primary and secondary liver cancer, the number and sizes of lesions, their locations within the Couinaud segments, and the volume and health status of the future liver remnant are key for informing treatment planning. Currently this is performed manually, generally by trained radiologists, who are seeing an inexorable growth in their workload. Integrating artificial intelligence (AI) and non-radiologist personnel into the workflow potentially addresses the increasing workload without sacrificing accuracy. This study evaluated the accuracy of non-radiologist technicians in liver cancer imaging compared with radiologists, both assisted by AI. METHODS: Non-contrast T1-weighted MRI data from 18 colorectal liver metastasis patients were analyzed using an AI-enabled decision support tool that enables non-radiology trained technicians to perform key liver measurements. Three non-radiologist, experienced operators and three radiologists performed whole liver segmentation, Couinaud segment segmentation, and the detection and measurements of lesions aided by AI-generated delineations. Agreement between radiologists and non-radiologists was assessed using the intraclass correlation coefficient (ICC). Two additional radiologists adjudicated any lesion detection discrepancies. RESULTS: Whole liver volume showed high levels of agreement between the non-radiologist and radiologist groups (ICC = 0.99). The Couinaud segment volumetry ICC range was 0.77-0.96. Both groups identified the same 41 lesions. As well, the non-radiologist group identified seven more structures which were also confirmed as lesions by the adjudicators. Lesion diameter categorization agreement was 90%, Couinaud localization 91.9%. Within-group variability was comparable for lesion measurements. CONCLUSION: 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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Assessing the performance of AI-assisted technicians in liver segmentation, Couinaud division, and lesion detection: a pilot study
- Date Crossref
- 10/08/2024
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Perspectum Ltd. (United Kingdom) pays non établi dans la noticeEntreprise
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Harvard University pays non établi dans la noticeUniversité ou école supérieure
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Massachusetts General Hospital Harvard Medical School pays non établi dans la noticeÉtablissement de santé
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Leiden University Department of Radiology pays non établi dans la noticeUniversité ou école supérieure
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Università della Svizzera italiana pays non établi dans la noticeUniversité ou école supérieure
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Oxford University Hospitals NHS Trust pays non établi dans la noticeÉtablissement de santé
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Clinica Di Radiologia EOC pays non établi dans la noticeÉtablissement de santé
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Oxford University Hospitals NHS Foundation Trust Department of Radiology pays non établi dans la noticeUniversité ou école supérieure
Perspectum Ltd. (United Kingdom), Harvard University et Harvard Medical School — Massachusetts General Hospital, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.