Artificial Intelligence in Orthopedics




•              Artificial intelligence (AI) provides machines with the ability to perform tasks using algorithms governed by pattern recognition and self-correction on large amounts of data to narrow options in order to avoid errors.

•              The 4 things necessary for AI in medicine include big data sets, powerful computers, cloud computing, and open-source algorithmic development.

•              The use of AI in health care continues to expand, and its impact on orthopedic surgery can already be found in diverse areas such as image recognition, risk prediction, patient-specific payment models, and clinical decision-making.

•              Just as the business of medicine was once considered outside the domain of the orthopedic surgeon, emerging technologies such as AI warrant ownership, leverage, and application by the orthopedic surgeon to improve the care that we provide to the patients we serve.

•              AI could provide solutions to factors contributing to physician burnout and medical mistakes. However, challenges regarding the ethical deployment, regulation, and the clinical superiority of AI over traditional statistics and decision-making remain to be resolved.

•              In their most simple form, AI applications in healthcare consist of a collection of technologies that will enable machines to comprehend, predict, act, and learn. Current technologies are limited because they are algorithm-based. Artificial Intelligence will make algorithm-only tools become indispensable instruments for patients, providers, and physicians.

Artificial Intelligence Applications in Orthopedics

•              AI has demonstrated high utility in classifying non-medical images. A study looked at the feasibility of using AI for skeletal radiographs to identify key image properties, despite the limited image quality. The AI program is composed of 5 deep learning networks, that were selected from a popular online library: the Caffe library (Jia et al. 2014). Caffe is a deep learning framework made with expression, speed, and modularity in mind

•              The study concluded that with AI it is possible to review images on an unprecedented scale in digital picture archives and link them to outcomes. Apart from identifying traditional orthopedic measures such as wrist angles algorithms can search for new patterns, for example, it is possible to go beyond simple angles into complex patterns that combine angles, comminuting, and bone quality. As many fracture classifications lack prognostic value, often with questionable inter-observer reliability, the option of aiding the classification using a computer algorithm is of great interest.

•              This AI program was compared against the radiography gold standard for fractures. The performance of the AI program was also compared with two orthopedic surgeons who reviewed the same images. AI program had an accuracy of at least 90 percent when identifying laterality, body part, and exam view. AI also performed comparably to the senior orthopedic surgeons’ image reviews. The study outcomes support the use of AI in orthopedic radiographs.

•              While the current AI technology does not provide important features surgeons need, such as advanced measurements, classifications, and the ability to combine multiple exam views, these are technical details that can be worked out in future iterations for the orthopedic surgeon community.

Will Orthopedic Surgeons be replaced by Robots in the Future?

•              Patients have so far shown great interest and enthusiasm for surgery assisted by robots, dazzled by the high-tech sophistication. Patients would still like humane orthopedic surgeons to be around them during the treatment process.

•              A large component of the physician/patient relationship, even for surgeons, is the communication, and that absolutely would be lost if artificial intelligence starts to make all the clinical decisions along with, or for the patient and then also performs the surgery.

•              Robotics and AI will be always there in the future and a synergistic relationship between the human mind and them is the way forward for more effective care of patients.

Since the 1950s when the term artificial intelligence was coined, its application and use have increased through rapid technological advances and have found their way into the health care sector, including orthopedics.

 Christopher P. Ames, MD, said artificial intelligence models now have the ability to preoperatively predict risks of major postoperative complications, which may aid real-time decision-making to proactively prevent reoperation or readmission.

A study published in 2018 showed the amount of orthopedic literature on machine learning, which is one type of artificial intelligence (AI), had an approximately tenfold increase since 2010, with the most frequently applied machine learning algorithms found in spine pathology, osteoarthritis detection, and prediction, and imaging of bone and cartilage.

An increase in larger datasets along with the convergence of cloud-based computing and graphical processing units (GPUs) with other areas of technology have allowed AI to become what it is today, according to Joseph H. Schwab, MD, chief of spine surgery and associate professor of orthopedic surgery at Harvard Medical School and Massachusetts General Hospital. He said convolution neural networks and deep learning algorithms have been used in image analysis while predictive models using machine learning algorithms have been used to identify outcomes of surgery or treatment.

AI across pathways of care

By using registry data, including demographic and co-morbidity information, preoperative disability mental health scores, and radiographic measurements, Christopher P. Ames, MD, of the University of California, San Francisco (UCSF), noted AI prediction models could be used to assist in real-time decision-making for patient care and treatment by preoperatively predicting major postoperative complications, as well as the risk of reoperation and of readmission based on patient and surgical factors. Models can also be created to predict the specific type of benefit of a surgical procedure to an individual patient including precise patient-weighted priorities, such as the ability to return to work or decreased pain medication usage, according to Ames.

Ames noted these predictive models also can be used to predict cost per procedure, which may help institutions protect their bottom line and enter into risk-sharing relationships with payers.

Intraoperatively, AI may lead to more accurate surgical decision-making at the time of surgery and improved patient outcomes by allowing surgeons to tailor each surgical experience to a particular patient.

Augmented or virtual reality types of AI may also be beneficial with rehabilitation protocols postoperatively by using a machine-learning algorithm to improve patient care, according to Schwab, who noted this could be especially beneficial for patients who live in rural areas.

Use in traumatic injury treatment

However, machine learning is not the only area where AI may provide benefits to patients with musculoskeletal injuries.

Stephen F. Badylak, DVM, Ph.D., MD, professor of surgery at McGowan Institute for Regenerative Medicine at the University of Pittsburgh, said AI may be able to identify whether tissues are healing from a traumatic injury will have a good or bad outcome based on certain biomarkers.

Feedback aids learning

AI may be used as a learning tool for orthopedic surgeons through its feedback mechanism, which Kamath noted can learn and develop over time.

Surgeons also can review benchmark models to compare their outcomes and complication rates to those of centers of excellence and learn which patients may benefit the most from which surgical procedures, Ames said.

Barriers of AI

 Despite the current interest in AI, Mark Alan Fontana, Ph.D., senior director of data science at Center for the Advancement of Value and Musculoskeletal Care at Hospital for Special Surgery, noted more research is needed before AI can make a bigger impact in orthopedics. This lack of research may be due to a lag in the use of technology in health care compared with other industries, he said.

One barrier to the use of AI in orthopedics is the learning models and algorithms are only as good or efficient as the data that are put in, according to Kamath.

Although the data that AI and predictive models compile can be a good source of hypothesis generation for research, Fontana noted these models can detect patterns in prior data and apply these patterns to newly input data, which may lead to predictive models giving advice that parrots societal and social biases within the data.

Furthermore, predictive models “tend to drift” or do worse as reality changes over time. They, therefore, require continuous upkeep, which may not always be possible depending on the organization. In addition, these models are not one-size-fits-all and need careful curation from hospital to hospital, according to Fontana.

The long set of operating instructions associated with predictive models “is time-consuming from a resource standpoint and from an understanding standpoint,” he said.

Because not every hospital will have the resources to run and upkeep these models, Badylak said the models must be developed in an understandable and user-friendly way, especially in light of the current barriers to implementing AI as a standard of care. He said regardless of whether they are user-friendly, the more advanced AI designed for the treatment of musculoskeletal injuries may be disadvantaged by regulatory barriers, as well as funding issues.

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