BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

214 related articles for article (PubMed ID: 35944352)

  • 1. Evaluation of physician performance using a concurrent-read artificial intelligence system to support breast ultrasound interpretation.
    Lai YC; Chen HH; Hsu JF; Hong YJ; Chiu TT; Chiou HJ
    Breast; 2022 Oct; 65():124-135. PubMed ID: 35944352
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis.
    Pinto MC; Rodriguez-Ruiz A; Pedersen K; Hofvind S; Wicklein J; Kappler S; Mann RM; Sechopoulos I
    Radiology; 2021 Sep; 300(3):529-536. PubMed ID: 34227882
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Improving the Performance of Radiologists Using Artificial Intelligence-Based Detection Support Software for Mammography: A Multi-Reader Study.
    Lee JH; Kim KH; Lee EH; Ahn JS; Ryu JK; Park YM; Shin GW; Kim YJ; Choi HY
    Korean J Radiol; 2022 May; 23(5):505-516. PubMed ID: 35434976
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time.
    Park EK; Kwak S; Lee W; Choi JS; Kooi T; Kim EK
    Radiol Artif Intell; 2024 May; 6(3):e230318. PubMed ID: 38568095
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 1000-Case Reader Study of Radiologists' Performance in Interpretation of Automated Breast Volume Scanner Images with a Computer-Aided Detection System.
    Xu X; Bao L; Tan Y; Zhu L; Kong F; Wang W
    Ultrasound Med Biol; 2018 Aug; 44(8):1694-1702. PubMed ID: 29853222
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.
    Rodríguez-Ruiz A; Krupinski E; Mordang JJ; Schilling K; Heywang-Köbrunner SH; Sechopoulos I; Mann RM
    Radiology; 2019 Feb; 290(2):305-314. PubMed ID: 30457482
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Breast cancer detection accuracy of AI in an entire screening population: a retrospective, multicentre study.
    Elhakim MT; Stougaard SW; Graumann O; Nielsen M; Lång K; Gerke O; Larsen LB; Rasmussen BSB
    Cancer Imaging; 2023 Dec; 23(1):127. PubMed ID: 38124111
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Improving mammography interpretation for both novice and experienced readers: a comparative study of two commercial artificial intelligence software.
    Kim HJ; Choi WJ; Gwon HY; Jang SJ; Chae EY; Shin HJ; Cha JH; Kim HH
    Eur Radiol; 2024 Jun; 34(6):3924-3934. PubMed ID: 37938383
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study.
    van Winkel SL; Rodríguez-Ruiz A; Appelman L; Gubern-Mérida A; Karssemeijer N; Teuwen J; Wanders AJT; Sechopoulos I; Mann RM
    Eur Radiol; 2021 Nov; 31(11):8682-8691. PubMed ID: 33948701
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms.
    Kühl J; Elhakim MT; Stougaard SW; Rasmussen BSB; Nielsen M; Gerke O; Larsen LB; Graumann O
    Eur Radiol; 2024 Jun; 34(6):3935-3946. PubMed ID: 37938386
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study.
    Rodriguez-Ruiz A; Lång K; Gubern-Merida A; Teuwen J; Broeders M; Gennaro G; Clauser P; Helbich TH; Chevalier M; Mertelmeier T; Wallis MG; Andersson I; Zackrisson S; Sechopoulos I; Mann RM
    Eur Radiol; 2019 Sep; 29(9):4825-4832. PubMed ID: 30993432
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography.
    Watanabe AT; Lim V; Vu HX; Chim R; Weise E; Liu J; Bradley WG; Comstock CE
    J Digit Imaging; 2019 Aug; 32(4):625-637. PubMed ID: 31011956
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Investigation of optimal use of computer-aided detection systems: the role of the "machine" in decision making process.
    Paquerault S; Hardy PT; Wersto N; Chen J; Smith RC
    Acad Radiol; 2010 Sep; 17(9):1112-21. PubMed ID: 20605489
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance.
    Kerschke L; Weigel S; Rodriguez-Ruiz A; Karssemeijer N; Heindel W
    Eur Radiol; 2022 Feb; 32(2):842-852. PubMed ID: 34383147
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience.
    Han X; Luo N; Xu L; Cao J; Guo N; He Y; Hong M; Jia X; Wang Z; Yang Z
    BMC Med Imaging; 2022 Feb; 22(1):28. PubMed ID: 35177029
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Artificial Intelligence Applied to Breast MRI for Improved Diagnosis.
    Jiang Y; Edwards AV; Newstead GM
    Radiology; 2021 Jan; 298(1):38-46. PubMed ID: 33078996
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Use of novel artificial intelligence computer-assisted detection (AI-CAD) for screening mammography: an analysis of 17,884 consecutive two-view full-field digital mammography screening exams.
    Heywang-Köbrunner SH; Hacker A; Jänsch A; Hertlein M; Mieskes C; Elsner S; Sinnatamby R; Katalinic A
    Acta Radiol; 2023 Oct; 64(10):2697-2703. PubMed ID: 37642981
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A deep learning-based automated diagnostic system for classifying mammographic lesions.
    Yamaguchi T; Inoue K; Tsunoda H; Uematsu T; Shinohara N; Mukai H
    Medicine (Baltimore); 2020 Jul; 99(27):e20977. PubMed ID: 32629712
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study.
    Kim YS; Jang MJ; Lee SH; Kim SY; Ha SM; Kwon BR; Moon WK; Chang JM
    Korean J Radiol; 2022 Dec; 23(12):1241-1250. PubMed ID: 36447412
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Improving digital breast tomosynthesis reading time: A pilot multi-reader, multi-case study using concurrent Computer-Aided Detection (CAD).
    Balleyguier C; Arfi-Rouche J; Levy L; Toubiana PR; Cohen-Scali F; Toledano AY; Boyer B
    Eur J Radiol; 2017 Dec; 97():83-89. PubMed ID: 29153373
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.