Dr. Ference’s Observations | AI and Knee Replacement
I spend a lot of time pouring over the literature about orthopedic surgery. As we are all “consumed” with the impact of AI on our world I thought I would share my thoughts on where AI might take us regarding knee replacement surgery. AI and Knee Replacement. Form More from Our Blog Click Here: Dr. Ference Blog
AI has the potential to significantly impact knee replacement surgery in several ways, enhancing various aspects of the procedure, patient care, and post-operative outcomes.
Here are some ways in which AI may influence knee replacement surgery:
- Preoperative Planning:
- Image Analysis: AI algorithms can analyze medical imaging, such as X-rays and MRI scans, to provide detailed information about the patient’s anatomy. This can help surgeons in planning the surgery more accurately by customizing the procedure to the patient’s specific anatomy.
- Surgical Assistance:
- Robotics: AI-powered robotic systems can assist surgeons during the procedure. These systems can offer precision and control, allowing for more accurate placement of implants.
- Navigation Systems: AI can be integrated into navigation systems to guide surgeons in real-time during the surgery, helping them achieve optimal implant alignment and positioning.
- Intraoperative Decision Support:
- Machine Learning Algorithms: AI can analyze real-time data during surgery, providing decision support to the surgeon. For example, algorithms can assess the balance of the knee and suggest adjustments to improve overall functionality.
- Postoperative Monitoring:
- Rehabilitation Planning: AI can assist in developing personalized rehabilitation plans based on patient data, optimizing the recovery process.
- Complication Prediction: By analyzing postoperative data, AI can help predict and prevent potential complications, allowing for early intervention and improved patient outcomes.
- Patient-Specific Implants:
- Custom Implants: AI can contribute to the design of patient-specific implants, taking into account individual anatomy and biomechanics. This can potentially improve the fit and longevity of the implant.
- Outcome Prediction:
- Predictive Analytics: AI algorithms can analyze a combination of preoperative and intraoperative data to predict postoperative outcomes, allowing for proactive measures to be taken to address potential issues.
- Learning and Improvement:
- Continuous Learning: AI systems can continuously learn from a large dataset of surgical procedures, incorporating new knowledge and improving their performance over time.
One good example of the current literature on the topic is below.
Artificial intelligence in knee arthroplasty: current concept of the available clinical applications
This is an article from the National Institute of Health.
Background
Artificial intelligence (AI) is defined as the study of algorithms that allow machines to reason and perform cognitive functions such as problem-solving, objects, images, word recognition, and decision-making. This study aimed to review the published articles and the comprehensive clinical relevance of AI-based tools used before, during, and after knee arthroplasty.
Methods
The search was conducted through PubMed, EMBASE, and MEDLINE databases from 2000 to 2021 using the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA).
Results
A total of 731 potential articles were reviewed, and 132 were included based on the inclusion criteria and exclusion criteria. Some steps of the knee arthroplasty procedure were assisted and improved by using AI-based tools. Before surgery, machine learning was used to aid surgeons in optimizing decision-making. During surgery, the robotic-assisted systems improved the accuracy of knee alignment, implant positioning, and ligamentous balance. After surgery, remote patient monitoring platforms helped to capture patients’ functional data.
Conclusion
In knee arthroplasty, the AI-based tools improve the decision-making process, surgical planning, accuracy, and repeatability of surgical procedures.
Here is a link to the article: AI & KNEE REPLACEMENT
My Thoughts on This Study
I love to share research to keep you informed on what is happening in our field. However, that does not mean I fully agree with each study. Let’s take the NIH study above. A few points of my own.
Unlike in the study above, where the patients are carefully selected, I work with all patients, no matter their condition going into surgery. I take the time needed to ensure that all of my patients get a superior outcome. And that means sometimes not following the plan that, for example, a Mako robot might suggest.
Patient Example
I’ll give you a real-life example. Today I performed a total knee replacement who was over 420 lbs. And this was her second knee. We did the first knee (to great success) six months ago. A high concentration of surgeons would not perform a knee replacement on a person with a BMI this high.
A surgeon’s level of experience is the most important factor as you decide on who will work on your knee. When you actually do surgery everybody’s anatomy is so different… you cannot simply depend on a computer. Even in today’s world, the technological aids are often wrong. A good surgeon can recognize this.
I love what AI is giving us and am particularly excited about where it takes us for knee revisions. More about this in a subsequent article.
Let’s Be Mindful That…
While AI holds great promise in enhancing knee replacement surgery, its implementation should be done cautiously. Ethical considerations, patient safety, and regulatory approval are crucial aspects to be addressed as AI technologies are integrated into surgical practices. Additionally, the effectiveness of AI in knee replacement surgery will likely depend on ongoing research, clinical trials, and collaborative efforts between healthcare professionals and technology developers.
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