دورية أكاديمية
The use of artificial intelligence to identify subjects with a positive FOBT predicted to be non-compliant with both colonoscopy and harbor cancer.
| العنوان: | The use of artificial intelligence to identify subjects with a positive FOBT predicted to be non-compliant with both colonoscopy and harbor cancer. |
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| المؤلفون: | Konikoff T; Division of Gastroenterology, Rabin Medical Center, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Israel., Flugelman A; Technion Israel Institute of Technology The Ruth and Bruce Rappaport Faculty of Medicine Haifa, Haifa, Israel., Comanesther D; Department of Quality Measurements and Research, Chief Physician's Office, Clalit Health Services, Tel-Aviv, Israel., Cohen AD; Department of Quality Measurements and Research, Chief Physician's Office, Clalit Health Services, Tel-Aviv, Israel; Siaal Research Center for Family Medicine and Primary Care, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel., Gingold-Belfer R; Division of Gastroenterology, Rabin Medical Center, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Israel., Boltin D; Division of Gastroenterology, Rabin Medical Center, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Israel., Golan MA; Division of Gastroenterology, Rabin Medical Center, Petah Tikva, Israel., Eizenstein S; Sackler Faculty of Medicine, Tel-Aviv University, Israel., Dotan I; Division of Gastroenterology, Rabin Medical Center, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Israel., Perry H; Department of Information Systems, Arison School of Business, Interdisciplinary Center, Herzliya, Israel., Levi Z; Division of Gastroenterology, Rabin Medical Center, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Israel. Electronic address: Zohar.levi.gastroenterology@gmail.com. |
| المصدر: | Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver [Dig Liver Dis] 2023 Sep; Vol. 55 (9), pp. 1253-1258. Date of Electronic Publication: 2023 Jun 05. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 100958385 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-3562 (Electronic) Linking ISSN: 15908658 NLM ISO Abbreviation: Dig Liver Dis Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2003- : Amsterdam : Elsevier Original Publication: Roma, Italy : Editrice gastroenterologica italiana, c2000- |
| مواضيع طبية MeSH: | Occult Blood* , Colorectal Neoplasms*/diagnosis , Colorectal Neoplasms*/epidemiology, Humans ; Artificial Intelligence ; Colonoscopy ; Early Detection of Cancer ; Mass Screening |
| مستخلص: | Background: Subjects with a positive Fecal Occult Blood Test (FOBT) that are non-compliant with colonoscopy are at increased risk for colorectal cancer (CRC). Yet, in clinical practice, many remain non-compliant. Aims: To evaluate whether machine learning models (ML) can identify subjects with a positive FOBT predicted to be both non-compliant with colonoscopy within six months and harbor CRC (defined as the "target population"). Methods: We trained and validated ML models based on extensive administrative and laboratory data about subjects with a positive FOBT between 2011 and 2013 within Clalit Health that were followed for cancer diagnosis up to 2018. Results: Out of 25,219 included subjects, 9,979(39.6%) were non-compliant with colonoscopy, and 202(0.8%) were both non-compliant and harbored cancer. Using ML, we reduced the number of subjects needed to engage from 25,219 to either 971 (3.85%) to identify 25.8%(52/202) of the target population, reducing the number needed to treat (NNT) from 124.8 to 19.4 or to 4,010(15,8%) to identify 55.0%(52/202) of the target population, NNT = 39.7. Conclusion: Machine learning technology may help healthcare organizations to identify subjects with a positive FOBT predicted to be both non-compliant with colonoscopy and harbor cancer from the first day of a positive FOBT with improved efficiency. Competing Interests: Conflict of interest All authors declare no conflict of interest. (Copyright © 2023 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Artificial intelligence; Colorectal cancer; Fecal occult blood test; Machine learning; Screening |
| تواريخ الأحداث: | Date Created: 20230607 Date Completed: 20230828 Latest Revision: 20231003 |
| رمز التحديث: | 20231003 |
| DOI: | 10.1016/j.dld.2023.04.027 |
| PMID: | 37286451 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1878-3562 |
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| DOI: | 10.1016/j.dld.2023.04.027 |