These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
172 related articles for article (PubMed ID: 35850498)
1. Differing benefits of artificial intelligence-based computer-aided diagnosis for breast US according to workflow and experience level. Lee SE; Han K; Youk JH; Lee JE; Hwang JY; Rho M; Yoon J; Kim EK; Yoon JH Ultrasonography; 2022 Oct; 41(4):718-727. PubMed ID: 35850498 [TBL] [Abstract][Full Text] [Related]
2. A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound: Added value for the inexperienced breast radiologist. Park HJ; Kim SM; La Yun B; Jang M; Kim B; Jang JY; Lee JY; Lee SH Medicine (Baltimore); 2019 Jan; 98(3):e14146. PubMed ID: 30653149 [TBL] [Abstract][Full Text] [Related]
3. Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection Software for Automated Breast Ultrasound. Kwon MR; Youn I; Lee MY; Lee HA Acad Radiol; 2024 Feb; 31(2):480-491. PubMed ID: 37813703 [TBL] [Abstract][Full Text] [Related]
4. Evaluation of the effect of computer aided diagnosis system on breast ultrasound for inexperienced radiologists in describing and determining breast lesions. Lee J; Kim S; Kang BJ; Kim SH; Park GE Med Ultrason; 2019 Aug; 21(3):239-245. PubMed ID: 31476202 [TBL] [Abstract][Full Text] [Related]
5. Evaluation of computer-aided diagnosis in breast ultrasonography: Improvement in diagnostic performance of inexperienced radiologists. Nicosia L; Addante F; Bozzini AC; Latronico A; Montesano M; Meneghetti L; Tettamanzi F; Frassoni S; Bagnardi V; De Santis R; Pesapane F; Fodor CI; Mastropasqua MG; Cassano E Clin Imaging; 2022 Feb; 82():150-155. PubMed ID: 34826773 [TBL] [Abstract][Full Text] [Related]
6. The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study. Wei Q; Yan YJ; Wu GG; Ye XR; Jiang F; Liu J; Wang G; Wang Y; Song J; Pan ZP; Hu JH; Jin CY; Wang X; Dietrich CF; Cui XW Eur Radiol; 2022 Jun; 32(6):4046-4055. PubMed ID: 35066633 [TBL] [Abstract][Full Text] [Related]
7. Application of Computer-Aided Diagnosis on Breast Ultrasonography: Evaluation of Diagnostic Performances and Agreement of Radiologists According to Different Levels of Experience. Cho E; Kim EK; Song MK; Yoon JH J Ultrasound Med; 2018 Jan; 37(1):209-216. PubMed ID: 28762552 [TBL] [Abstract][Full Text] [Related]
8. Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance. Ma S; Li Y; Yin J; Niu Q; An Z; Du L; Li F; Gu J Front Oncol; 2024; 14():1374278. PubMed ID: 38756651 [TBL] [Abstract][Full Text] [Related]
9. Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance. Dratsch T; Chen X; Rezazade Mehrizi M; Kloeckner R; Mähringer-Kunz A; Püsken M; Baeßler B; Sauer S; Maintz D; Pinto Dos Santos D Radiology; 2023 May; 307(4):e222176. PubMed ID: 37129490 [TBL] [Abstract][Full Text] [Related]
10. Diagnostic Value of Breast Lesions Between Deep Learning-Based Computer-Aided Diagnosis System and Experienced Radiologists: Comparison the Performance Between Symptomatic and Asymptomatic Patients. Xiao M; Zhao C; Li J; Zhang J; Liu H; Wang M; Ouyang Y; Zhang Y; Jiang Y; Zhu Q Front Oncol; 2020; 10():1070. PubMed ID: 32733799 [No Abstract] [Full Text] [Related]
11. Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography. Choi JS; Han BK; Ko ES; Bae JM; Ko EY; Song SH; Kwon MR; Shin JH; Hahn SY Korean J Radiol; 2019 May; 20(5):749-758. PubMed ID: 30993926 [TBL] [Abstract][Full Text] [Related]
12. Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging. Zhang N; Li XT; Ma L; Fan ZQ; Sun YS Clin Imaging; 2021 Nov; 79():56-63. PubMed ID: 33887507 [TBL] [Abstract][Full Text] [Related]
13. Application of ultrasonic dual-mode artificially intelligent architecture in assisting radiologists with different diagnostic levels on breast masses classification. Li C; Li J; Tan T; Chen K; Xu Y; Wu R Diagn Interv Radiol; 2021 May; 27(3):315-322. PubMed ID: 34003119 [TBL] [Abstract][Full Text] [Related]
14. The added value of an artificial intelligence system in assisting radiologists on indeterminate BI-RADS 0 mammograms. Yi C; Tang Y; Ouyang R; Zhang Y; Cao Z; Yang Z; Wu S; Han M; Xiao J; Chang P; Ma J Eur Radiol; 2022 Mar; 32(3):1528-1537. PubMed ID: 34528107 [TBL] [Abstract][Full Text] [Related]
15. AI-CAD for differentiating lesions presenting as calcifications only on mammography: outcome analysis incorporating the ACR BI-RADS descriptors for calcifications. Yoon J; Lee HS; Kim MJ; Park VY; Kim EK; Yoon JH Eur Radiol; 2022 Oct; 32(10):6565-6574. PubMed ID: 35748900 [TBL] [Abstract][Full Text] [Related]
16. Application of Artificial Intelligence Computer-Assisted Diagnosis Originally Developed for Thyroid Nodules to Breast Lesions on Ultrasound. Lee SE; Lee E; Kim EK; Yoon JH; Park VY; Youk JH; Kwak JY J Digit Imaging; 2022 Dec; 35(6):1699-1707. PubMed ID: 35902445 [TBL] [Abstract][Full Text] [Related]
17. Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Choi JH; Kang BJ; Baek JE; Lee HS; Kim SH Ultrasonography; 2018 Jul; 37(3):217-225. PubMed ID: 28992680 [TBL] [Abstract][Full Text] [Related]
18. Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study. Yongping L; Juan Z; Zhou P; Yongfeng Z; Liu W; Shi Y JMIR Med Inform; 2020 May; 8(5):e18251. PubMed ID: 32369039 [TBL] [Abstract][Full Text] [Related]
19. Mammographic density assessment: comparison of radiologists, automated volumetric measurement, and artificial intelligence-based computer-assisted diagnosis. Eom HJ; Cha JH; Choi WJ; Cho SM; Jin K; Kim HH Acta Radiol; 2024 Jul; 65(7):708-715. PubMed ID: 38825883 [TBL] [Abstract][Full Text] [Related]
20. Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists' Diagnosis Performance? An Observer Study. He Z; Li Y; Zeng W; Xu W; Liu J; Ma X; Wei J; Zeng H; Xu Z; Wang S; Wen C; Wu J; Feng C; Ma M; Qin G; Lu Y; Chen W Front Oncol; 2021; 11():773389. PubMed ID: 34976817 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]