Artificial intelligence, machine learning, and deep learning in orthopedic surgery

Artificial intelligence (AI) is intelligence demonstrated by machines. The main goal of AI is not to replace healthcare professionals, but to enable better patient experience and better inform the clinical decision-making process to improve the safety of patients and reliability of clinicians. The main principle is based on the following assertion: computers can precisely mimic cognitive functions of human beings such as learning and problem solving.[1] The AI involves machine learning in the form of deep learning.

Machine learning is a form of AI using computational algorithms that can learn from large data sets and make predictions.

Deep learning uses convolutional neural network (CNN) architecture. The CNN is an artificial neural network (ANN) algorithm that applies multilayer neural network structure.[2]

Deep learning may be a beneficial assistant in the detection of bone fractures, for the evaluation of cervical arthrosis and robotic joint arthroplasty.[1-3] However, the public unawareness of the doubtful outcome superiority associated with robotic-assisted orthopedic surgery may contribute to misinformed decisions in some patients. For a better evaluation of the utility of robotic joint arthroplasty, further well-designed, prospective, controlled studies with long-term follow-up would be helpful.[4]

Finally, even a smartphone application that can measure the accelerometer-based spatiotemporal gait parameters in a valid and safe manner is available, which eliminates the need for a computer, engineering knowledge, or additional applications.[5,6]

In conclusion, the ethical and legal issues and the clinical superiority of AI over decision-making remain to be resolved with further well-planned scientific studies.

Citation: Atik OŞ. Artificial intelligence, machine learning, and deep learning in orthopedic surgery. Jt Dis Relat Surg 2022;33(2):484-485.

Data Sharing Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.

The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.

The authors received no financial support for the research and/or authorship of this article.

References

1. Beyaz S. A brief history of artificial intelligence and robotic surgery in orthopedics & traumatology and future expectations. Jt Dis Relat Surg 2020;31:653-5.
2. Maraş Y, Tokdemir G, Üreten K, Atalar E, Duran S, Maraş H. Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods. Jt Dis Relat Surg 2022;33:93-101.
3. Burger JA, Kleeblad LJ, Laas N, Pearle AD. Mid-term survivorship and patient-reported outcomes of robotic-arm assisted partial knee arthroplasty. Bone Joint J 2020;102- B:108-16.
4. Atik OŞ. Does the use of robotic technology in hip arthroplasty provide superior clinical outcomes? Jt Dis Relat Surg 2022;33:253-4.
5. Silsupadol P, Prupetkaew P, Kamnardsiri T, Lugade V. Smartphone-based assessment of gait during straight walking, turning, and walking speed modulation inlaboratory and free-living environments. IEEE J Biomed Health Inform 2020;24:1188-95.
6. Çankaya D, Aktı S, Ünal ŞB, Sezgin EA. Unicompartmental knee arthroplasty results in a better gait pattern than total knee arthroplasty: Gait analysis with a smartphone application. Jt Dis Relat Surg 2021;32:22-7.