| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A deep learning approach to automatically quantify lower extremity alignment in children
|
Tsai, Andy
|
|
|
51 |
2 |
p. 381-390
|
artikel
|
| 2 |
AI MSK clinical applications: cartilage and osteoarthritis
|
Joseph, Gabby B.
|
|
|
51 |
2 |
p. 331-343
|
artikel
|
| 3 |
AI MSK clinical applications: orthopedic implants
|
Yi, Paul H.
|
|
|
51 |
2 |
p. 305-313
|
artikel
|
| 4 |
AI MSK clinical applications: spine imaging
|
Huber, Florian A.
|
|
|
51 |
2 |
p. 279-291
|
artikel
|
| 5 |
AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?
|
Shin, YiRang
|
|
|
51 |
2 |
p. 293-304
|
artikel
|
| 6 |
Artificial intelligence applied to musculoskeletal oncology: a systematic review
|
Li, Matthew D.
|
|
|
51 |
2 |
p. 245-256
|
artikel
|
| 7 |
Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches
|
Fritz, Benjamin
|
|
|
51 |
2 |
p. 315-329
|
artikel
|
| 8 |
Artificial intelligence in musculoskeletal imaging: a perspective on value propositions, clinical use, and obstacles
|
Fritz, Jan
|
|
|
51 |
2 |
p. 239-243
|
artikel
|
| 9 |
Artificial intelligence in orthopedic implant model classification: a systematic review
|
Ren, Mark
|
|
|
51 |
2 |
p. 407-416
|
artikel
|
| 10 |
Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network
|
Chang, Connie Y.
|
|
|
51 |
2 |
p. 391-399
|
artikel
|
| 11 |
Can AI distinguish a bone radiograph from photos of flowers or cars? Evaluation of bone age deep learning model on inappropriate data inputs
|
Yi, Paul H.
|
|
|
51 |
2 |
p. 401-406
|
artikel
|
| 12 |
Clinical applications of AI in MSK imaging: a liability perspective
|
Harvey, H. Benjamin
|
|
|
51 |
2 |
p. 235-238
|
artikel
|
| 13 |
Deciphering musculoskeletal artificial intelligence for clinical applications: how do I get started?
|
Mutasa, Simukayi
|
|
|
51 |
2 |
p. 271-278
|
artikel
|
| 14 |
Deep learning approach to predict pain progression in knee osteoarthritis
|
Guan, Bochen
|
|
|
51 |
2 |
p. 363-373
|
artikel
|
| 15 |
Deep learning detection of subtle fractures using staged algorithms to mimic radiologist search pattern
|
Ren, Mark
|
|
|
51 |
2 |
p. 345-353
|
artikel
|
| 16 |
Deep learning for accurately recognizing common causes of shoulder pain on radiographs
|
Grauhan, Nils F.
|
|
|
51 |
2 |
p. 355-362
|
artikel
|
| 17 |
Introduction to the special issue on artificial intelligence in musculoskeletal radiology
|
Chang, Connie Y.
|
|
|
51 |
2 |
p. 233
|
artikel
|
| 18 |
Mammary-type myofibroblastoma of the thigh mimicking liposarcoma
|
Akhlaq, Natasha
|
|
|
51 |
2 |
p. 441-445
|
artikel
|
| 19 |
Musculoskeletal trauma and artificial intelligence: current trends and projections
|
Laur, Olga
|
|
|
51 |
2 |
p. 257-269
|
artikel
|
| 20 |
Natural language processing of radiology reports to investigate the effects of the COVID-19 pandemic on the incidence and age distribution of fractures
|
Jungmann, Florian
|
|
|
51 |
2 |
p. 375-380
|
artikel
|
| 21 |
Pachydermodactyly: the role of ultrasonography, superb microvascular imaging, and elastography in diagnosis
|
Novais, Cláudia Martins
|
|
|
51 |
2 |
p. 435-439
|
artikel
|
| 22 |
Predicting long-term outcomes of ultrasound-guided percutaneous irrigation of calcific tendinopathy with the use of machine learning
|
Vassalou, Evangelia E.
|
|
|
51 |
2 |
p. 417-422
|
artikel
|
| 23 |
Test yourself-answer: multiple facial skin lesions associated with gingival hypertrophy in a pair of siblings
|
Marroun, Nour
|
|
|
51 |
2 |
p. 447-450
|
artikel
|
| 24 |
Test Yourself-Question: Multiple facial skin lesions associated with gingival hypertrophy in a pair of siblings
|
Marroun, Nour
|
|
|
51 |
2 |
p. 431-434
|
artikel
|
| 25 |
Your mileage may vary: impact of data input method for a deep learning bone age app’s predictions
|
Yi, Paul H.
|
|
|
51 |
2 |
p. 423-429
|
artikel
|
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