135 related articles for article (PubMed ID: 32090207)
1. Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge.
Pan I; Thodberg HH; Halabi SS; Kalpathy-Cramer J; Larson DB
Radiol Artif Intell; 2019 Nov; 1(6):e190053. PubMed ID: 32090207
[TBL] [Abstract][Full Text] [Related]
2. The RSNA Pediatric Bone Age Machine Learning Challenge.
Halabi SS; Prevedello LM; Kalpathy-Cramer J; Mamonov AB; Bilbily A; Cicero M; Pan I; Pereira LA; Sousa RT; Abdala N; Kitamura FC; Thodberg HH; Chen L; Shih G; Andriole K; Kohli MD; Erickson BJ; Flanders AE
Radiology; 2019 Feb; 290(2):498-503. PubMed ID: 30480490
[TBL] [Abstract][Full Text] [Related]
3. Evaluating the Robustness of a Deep Learning Bone Age Algorithm to Clinical Image Variation Using Computational Stress Testing.
Santomartino SM; Putman K; Beheshtian E; Parekh VS; Yi PH
Radiol Artif Intell; 2024 May; 6(3):e230240. PubMed ID: 38477660
[TBL] [Abstract][Full Text] [Related]
4. Generalizability and Bias in a Deep Learning Pediatric Bone Age Prediction Model Using Hand Radiographs.
Beheshtian E; Putman K; Santomartino SM; Parekh VS; Yi PH
Radiology; 2023 Feb; 306(2):e220505. PubMed ID: 36165796
[TBL] [Abstract][Full Text] [Related]
5. Rethinking Greulich and Pyle: A Deep Learning Approach to Pediatric Bone Age Assessment Using Pediatric Trauma Hand Radiographs.
Pan I; Baird GL; Mutasa S; Merck D; Ruzal-Shapiro C; Swenson DW; Ayyala RS
Radiol Artif Intell; 2020 Jul; 2(4):e190198. PubMed ID: 33937834
[TBL] [Abstract][Full Text] [Related]
6. Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs.
Larson DB; Chen MC; Lungren MP; Halabi SS; Stence NV; Langlotz CP
Radiology; 2018 Apr; 287(1):313-322. PubMed ID: 29095675
[TBL] [Abstract][Full Text] [Related]
7. Enhancement of Non-Linear Deep Learning Model by Adjusting Confounding Variables for Bone Age Estimation in Pediatric Hand X-rays.
Kim KD; Kyung S; Jang M; Ji S; Lee DH; Yoon HM; Kim N
J Digit Imaging; 2023 Oct; 36(5):2003-2014. PubMed ID: 37268839
[TBL] [Abstract][Full Text] [Related]
8. Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists.
Reddy NE; Rayan JC; Annapragada AV; Mahmood NF; Scheslinger AE; Zhang W; Kan JH
Pediatr Radiol; 2020 Apr; 50(4):516-523. PubMed ID: 31863193
[TBL] [Abstract][Full Text] [Related]
9. Development and Evaluation of Deep Learning Models for Automated Estimation of Myelin Maturation Using Pediatric Brain MRI Scans.
Akinci D'Antonoli T; Todea RA; Leu N; Datta AN; Stieltjes B; Pruefer F; Wasserthal J
Radiol Artif Intell; 2023 Sep; 5(5):e220292. PubMed ID: 37795138
[TBL] [Abstract][Full Text] [Related]
10. MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling.
Mutasa S; Chang PD; Ruzal-Shapiro C; Ayyala R
J Digit Imaging; 2018 Aug; 31(4):513-519. PubMed ID: 29404850
[TBL] [Abstract][Full Text] [Related]
11. Modality-specific deep learning model ensembles toward improving TB detection in chest radiographs.
Rajaraman S; Antani SK
IEEE Access; 2020; 8():27318-27326. PubMed ID: 32257736
[TBL] [Abstract][Full Text] [Related]
12. Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis.
Zucker EJ; Barnes ZA; Lungren MP; Shpanskaya Y; Seekins JM; Halabi SS; Larson DB
J Cyst Fibros; 2020 Jan; 19(1):131-138. PubMed ID: 31056440
[TBL] [Abstract][Full Text] [Related]
13. Deep Learning Measurement of Leg Length Discrepancy in Children Based on Radiographs.
Zheng Q; Shellikeri S; Huang H; Hwang M; Sze RW
Radiology; 2020 Jul; 296(1):152-158. PubMed ID: 32315267
[TBL] [Abstract][Full Text] [Related]
14. Performance and Usability of Code-Free Deep Learning for Chest Radiograph Classification, Object Detection, and Segmentation.
Santomartino SM; Hafezi-Nejad N; Parekh VS; Yi PH
Radiol Artif Intell; 2023 Mar; 5(2):e220062. PubMed ID: 37035428
[TBL] [Abstract][Full Text] [Related]
15. Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: A benchmarking study.
Dong T; Sinha S; Zhai B; Fudulu DP; Chan J; Narayan P; Judge A; Caputo M; Dimagli A; Benedetto U; Angelini GD
Digit Health; 2023; 9():20552076231187605. PubMed ID: 37492033
[TBL] [Abstract][Full Text] [Related]
16. Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.
von Schacky CE; Wilhelm NJ; Schäfer VS; Leonhardt Y; Gassert FG; Foreman SC; Gassert FT; Jung M; Jungmann PM; Russe MF; Mogler C; Knebel C; von Eisenhart-Rothe R; Makowski MR; Woertler K; Burgkart R; Gersing AS
Radiology; 2021 Nov; 301(2):398-406. PubMed ID: 34491126
[TBL] [Abstract][Full Text] [Related]
17. Development and Validation of a Convolutional Neural Network for Automated Detection of Scaphoid Fractures on Conventional Radiographs.
Hendrix N; Scholten E; Vernhout B; Bruijnen S; Maresch B; de Jong M; Diepstraten S; Bollen S; Schalekamp S; de Rooij M; Scholtens A; Hendrix W; Samson T; Sharon Ong LL; Postma E; van Ginneken B; Rutten M
Radiol Artif Intell; 2021 Jul; 3(4):e200260. PubMed ID: 34350413
[TBL] [Abstract][Full Text] [Related]
18. Construction of artificial intelligence system of carpal bone age for Chinese children based on China-05 standard.
Zhao X; Zhang M; Cheng M; Yue X; Li W; Li C
Med Phys; 2022 May; 49(5):3223-3232. PubMed ID: 35181886
[TBL] [Abstract][Full Text] [Related]
19. Hanging protocol optimization of lumbar spine radiographs with machine learning.
Kitamura G
Skeletal Radiol; 2021 Sep; 50(9):1809-1819. PubMed ID: 33590305
[TBL] [Abstract][Full Text] [Related]
20. An artificial intelligence-based bone age assessment model for Han and Tibetan children.
Liu Q; Wang H; Wangjiu C; Awang T; Yang M; Qiongda P; Yang X; Pan H; Wang F
Front Physiol; 2024; 15():1329145. PubMed ID: 38426209
[No Abstract] [Full Text] [Related]
[Next] [New Search]