YouTube as a Source of Patient Information for Knee Osteoarthritis Exercises

Fatih Bağcıer; Elem Yorulmaz; Mustafa Hüseyin Temel

doi:10.4274/tod.galenos.2021.48569

YouTube as a Source of Patient Information for Knee Osteoarthritis Exercises

Turkish Journal of Osteoporosis ◽

10.4274/tod.galenos.2021.48569 ◽

2021 ◽

Vol 27 (3) ◽

pp. 133-139

Author(s):

Fatih Bağcıer ◽

Elem Yorulmaz ◽

Mustafa Hüseyin Temel

Keyword(s):

Knee Osteoarthritis ◽

Patient Information

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YouTube is a poor source of patient information for knee arthroplasty and knee osteoarthritis

Arthroplasty Today ◽

10.1016/j.artd.2018.09.010 ◽

2019 ◽

Vol 5 (1) ◽

pp. 78-82 ◽

Cited By ~ 6

Author(s):

Michael Wong ◽

Bhumit Desai ◽

Michele Bautista ◽

Ohmin Kwon ◽

Nicholas Kolodychuk ◽

...

Keyword(s):

Knee Osteoarthritis ◽

Knee Arthroplasty ◽

Patient Information

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Can Additional Patient Information Improve the Diagnostic Performance of Deep Learning for the Interpretation of Knee Osteoarthritis Severity

Journal of Clinical Medicine ◽

10.3390/jcm9103341 ◽

2020 ◽

Vol 9 (10) ◽

pp. 3341

Author(s):

Dong Hyun Kim ◽

Kyong Joon Lee ◽

Dongjun Choi ◽

Jae Ik Lee ◽

Han Gyeol Choi ◽

...

Keyword(s):

Deep Learning ◽

Confidence Interval ◽

Knee Osteoarthritis ◽

Patient Information ◽

Diagnostic Performance ◽

Characteristic Curve ◽

Image Data ◽

Clinical Information ◽

Additional Patient ◽

Test Set

The study compares the diagnostic performance of deep learning (DL) with that of the former radiologist reading of the Kellgren–Lawrence (KL) grade and evaluates whether additional patient data can improve the diagnostic performance of DL. From March 2003 to February 2017, 3000 patients with 4366 knee AP radiographs were randomly selected. DL was trained using knee images and clinical information in two stages. In the first stage, DL was trained only with images and then in the second stage, it was trained with image data and clinical information. In the test set of image data, the areas under the receiver operating characteristic curve (AUC)s of the DL algorithm in diagnosing KL 0 to KL 4 were 0.91 (95% confidence interval (CI), 0.88–0.95), 0.80 (95% CI, 0.76–0.84), 0.69 (95% CI, 0.64–0.73), 0.86 (95% CI, 0.83–0.89), and 0.96 (95% CI, 0.94–0.98), respectively. In the test set with image data and additional patient information, the AUCs of the DL algorithm in diagnosing KL 0 to KL 4 were 0.97 (95% confidence interval (CI), 0.71–0.74), 0.85 (95% CI, 0.80–0.86), 0.75 (95% CI, 0.66–0.73), 0.86 (95% CI, 0.79–0.85), and 0.95 (95% CI, 0.91–0.97), respectively. The diagnostic performance of image data with additional patient information showed a statistically significantly higher AUC than image data alone in diagnosing KL 0, 1, and 2 (p-values were 0.008, 0.020, and 0.027, respectively).The diagnostic performance of DL was comparable to that of the former radiologist reading of the knee osteoarthritis KL grade. Additional patient information improved DL diagnosis in interpreting early knee osteoarthritis.

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