166 related articles for article (PubMed ID: 34074037)
21. Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features.
Gomes Ataide EJ; Ponugoti N; Illanes A; Schenke S; Kreissl M; Friebe M
Sensors (Basel); 2020 Oct; 20(21):. PubMed ID: 33121054
[TBL] [Abstract][Full Text] [Related]
22. Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network.
Chi J; Walia E; Babyn P; Wang J; Groot G; Eramian M
J Digit Imaging; 2017 Aug; 30(4):477-486. PubMed ID: 28695342
[TBL] [Abstract][Full Text] [Related]
23. Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis: A Multi-Cohort Study.
Zhu YC; Du H; Jiang Q; Zhang T; Huang XJ; Zhang Y; Shi XR; Shan J; AlZoubi A
J Ultrasound Med; 2022 Aug; 41(8):1961-1974. PubMed ID: 34751458
[TBL] [Abstract][Full Text] [Related]
24. Combination of ultrasound elastography with TI-RADS in the diagnosis of small thyroid nodules (≤10 mm): A new method to increase the diagnostic performance.
Du YR; Ji CL; Wu Y; Gu XG
Eur J Radiol; 2018 Dec; 109():33-40. PubMed ID: 30527309
[TBL] [Abstract][Full Text] [Related]
25. Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained FNAB cytological images.
Gopinath B; Shanthi N
Australas Phys Eng Sci Med; 2013 Jun; 36(2):219-30. PubMed ID: 23690210
[TBL] [Abstract][Full Text] [Related]
26. Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists.
Buda M; Wildman-Tobriner B; Hoang JK; Thayer D; Tessler FN; Middleton WD; Mazurowski MA
Radiology; 2019 Sep; 292(3):695-701. PubMed ID: 31287391
[TBL] [Abstract][Full Text] [Related]
27. Qualitative analysis of contrast-enhanced ultrasound in the diagnosis of small, TR3-5 benign and malignant thyroid nodules measuring ≤1 cm.
Li X; Gao F; Li F; Han XX; Shao SH; Yao MH; Li CX; Zheng J; Wu R; Du LF
Br J Radiol; 2020 Jul; 93(1111):20190923. PubMed ID: 32242748
[TBL] [Abstract][Full Text] [Related]
28. Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility.
Wildman-Tobriner B; Buda M; Hoang JK; Middleton WD; Thayer D; Short RG; Tessler FN; Mazurowski MA
Radiology; 2019 Jul; 292(1):112-119. PubMed ID: 31112088
[TBL] [Abstract][Full Text] [Related]
29. Computer-aided diagnosis of malignant or benign thyroid nodes based on ultrasound images.
Yu Q; Jiang T; Zhou A; Zhang L; Zhang C; Xu P
Eur Arch Otorhinolaryngol; 2017 Jul; 274(7):2891-2897. PubMed ID: 28389809
[TBL] [Abstract][Full Text] [Related]
30. SK-Unet++: An improved Unet++ network with adaptive receptive fields for automatic segmentation of ultrasound thyroid nodule images.
Dai H; Xie W; Xia E
Med Phys; 2024 Mar; 51(3):1798-1811. PubMed ID: 37606374
[TBL] [Abstract][Full Text] [Related]
31. AIBx, Artificial Intelligence Model to Risk Stratify Thyroid Nodules.
Thomas J; Haertling T
Thyroid; 2020 Jun; 30(6):878-884. PubMed ID: 32013775
[No Abstract] [Full Text] [Related]
32. Computer-Aided Diagnosis System for the Evaluation of Thyroid Nodules on Ultrasonography: Prospective Non-Inferiority Study according to the Experience Level of Radiologists.
Chung SR; Baek JH; Lee MK; Ahn Y; Choi YJ; Sung TY; Song DE; Kim TY; Lee JH
Korean J Radiol; 2020 Mar; 21(3):369-376. PubMed ID: 32090529
[TBL] [Abstract][Full Text] [Related]
33. The Diagnostic Value of Artificial Intelligence Ultrasound S-Detect Technology for Thyroid Nodules.
Huang P; Zheng B; Li M; Xu L; Rabbani S; Mayet AM; Chen C; Zhan B; Jun H
Comput Intell Neurosci; 2022; 2022():3656572. PubMed ID: 36471665
[TBL] [Abstract][Full Text] [Related]
34. Deep learning diagnostic performance and visual insights in differentiating benign and malignant thyroid nodules on ultrasound images.
Liu Y; Feng Y; Qian L; Wang Z; Hu X
Exp Biol Med (Maywood); 2023 Dec; 248(24):2538-2546. PubMed ID: 38279511
[TBL] [Abstract][Full Text] [Related]
35. Comparison of diagnostic accuracy and utility of artificial intelligence-optimized ACR TI-RADS and original ACR TI-RADS: a multi-center validation study based on 2061 thyroid nodules.
Liu Y; Li X; Yan C; Liu L; Liao Y; Zeng H; Huang W; Li Q; Tao N; Zhou J
Eur Radiol; 2022 Nov; 32(11):7733-7742. PubMed ID: 35505119
[TBL] [Abstract][Full Text] [Related]
36. Shape-margin knowledge augmented network for thyroid nodule segmentation and diagnosis.
Liu W; Lin C; Chen D; Niu L; Zhang R; Pi Z
Comput Methods Programs Biomed; 2024 Feb; 244():107999. PubMed ID: 38194766
[TBL] [Abstract][Full Text] [Related]
37. Reduction in Thyroid Nodule Biopsies and Improved Accuracy with American College of Radiology Thyroid Imaging Reporting and Data System.
Hoang JK; Middleton WD; Farjat AE; Langer JE; Reading CC; Teefey SA; Abinanti N; Boschini FJ; Bronner AJ; Dahiya N; Hertzberg BS; Newman JR; Scanga D; Vogler RC; Tessler FN
Radiology; 2018 Apr; 287(1):185-193. PubMed ID: 29498593
[TBL] [Abstract][Full Text] [Related]
38. Clinical diagnostic value of contrast-enhanced ultrasound and TI-RADS classification for benign and malignant thyroid tumors: One comparative cohort study.
Xu Y; Qi X; Zhao X; Ren W; Ding W
Medicine (Baltimore); 2019 Jan; 98(4):e14051. PubMed ID: 30681562
[TBL] [Abstract][Full Text] [Related]
39. Utility of adult-based ultrasound malignancy risk stratifications in pediatric thyroid nodules.
Martinez-Rios C; Daneman A; Bajno L; van der Kaay DCM; Moineddin R; Wasserman JD
Pediatr Radiol; 2018 Jan; 48(1):74-84. PubMed ID: 28983667
[TBL] [Abstract][Full Text] [Related]
40. An integrated AI model to improve diagnostic accuracy of ultrasound and output known risk features in suspicious thyroid nodules.
Wang J; Jiang J; Zhang D; Zhang YZ; Guo L; Jiang Y; Du S; Zhou Q
Eur Radiol; 2022 Mar; 32(3):2120-2129. PubMed ID: 34657970
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
