Deep Learning

Artificial intelligence (AI) has revolutionized the field of medicine and has proposed deep learning (DL) algorithms, a higher-level machine learning (ML) approach, to serve as an aiding tool for orthopedic surgeons in the operation room. Its vast potential continues to excite surgeons for the major impact it hopes to bring in the workforce. Unlike ML, DL can extract features autonomously from raw datasets, learning and adapting without manual training from a programmer. It does this by gathering a vast amount of processed input and creating artificial neural networks to extract highly precise output data. The current advancements in DL have enabled orthopedic surgeons to apply this novel innovation to analyze images and detect fractures, tumors, and implants, as well as classify and localize pathologies. Because orthopedic surgery is a field that mostly relies on technological and industrial growth, DL provides the opportunity to use its applications in this field allowing surgeons to decrease workload and increase efficiency during patient care. If DL continues to expand its practical application, it will come near to achieving surgeon-level accuracy in its role in diagnostic and treatment plans. This will decrease the burden of time-consuming and tiring tasks off surgeons and will enable optimal patient care in the workforce. However, the increasing trend of DL use also questions how reliable they are. These models are designed to minimize missed diagnoses and surgeon-made errors but are not ready to be used alone but perhaps as an aiding tool. The vast amount of data used is vital to regulate as it may contain biased information and may not always apply to specific groups of patient populations. In the meantime, the input data must be secured to protect patient privacy and keep ethical concerns in mind. This chapter focuses on the definition of DL, how algorithms are processed, its applications, drawbacks, and what the future holds for DL in orthopedic surgery.