Concerns surrounding application of artificial intelligence in hip and knee arthroplasty

Operative or Nonoperative Treatment is Predicted Accurately for Patients Who Have Hip Complaints Consulting an Orthopedic Surgeon Using Machine Learning Algorithms Trained With Prehospital Acquired History-Taking Data

BACKGROUND

Increasing numbers of patients suffering from hip osteoartritis will lead to increased orthopaedic health care consumption. Artificial intelligence might alleviate this problem, using Machine learning (ML) to optimize orthopaedic consultation workflow by predicting treatment strategy (non-operative or operative) prior to consultation. The purpose of this study was to assess ML accuracy in clinical practice, by comparing ML predictions to the outcome of clinical consultations.

METHODS

In this prospective clinical cohort study, adult patients referred for hip complaints between January 20th to February 20th 2023 were included. Patients completed a computer-assisted history taking (CAHT) form and using these CAHT answers, a ML-algorithm predicted non-operative or operative treatment outcome prior to in-hospital consultation. During consultation, orthopaedic surgeons and physician assistants were blinded to the prediction in 90 and unblinded in 29 cases. Consultation outcome (non-operative or operative) was compared to ML treatment prediction for all cases, and for blinded and unblinded conditions separately. Analysis was done on 119 consultations.

RESULTS

Overall treatment strategy prediction was correct in 101 cases (accuracy 85%, P < .0001). Non-operative treatment prediction (n = 71) was 97% correct versus 67% for operative treatment prediction (n = 48). Results from unblinded consultations (86.2% correct predictions,) were not statistically different from blinded consultations (84.4% correct, P > .05).

CONCLUSIONS

Machine Learning algorithms can predict non-operative or operative treatment for patients with hip complaints with high accuracy. This could facilitate scheduling of non-operative patients with physician assistants, and operative patients with orthopaedic surgeons including direct access to pre-operative screening, thereby optimizing usage of health care resources.

Predictive Tool Use and Willingness for Surgery in Patients With Knee Osteoarthritis: A Randomized Clinical Trial

Importance

Despite the increasing number of tools available to predict the outcomes of total knee arthroplasty (TKA), the effect of these predictive tools on patient decision-making remains uncertain.

Objective

To assess the effect of an online predictive tool on patient-reported willingness to undergo TKA.

Design, Setting, and Participants

This parallel, double-masked, 2-arm randomized clinical trial compared predictive tool use with treatment as usual (TAU). The study was conducted between June 30, 2022, and July 31, 2023. Participants were followed up for 6 months after enrollment. Participants were recruited from a major Australian private health insurance company and from the surgical waiting list for publicly funded TKA at a tertiary hospital. Eligible participants had unilateral knee osteoarthritis, were contemplating TKA, and had previously tried nonsurgical interventions, such as lifestyle modifications, physiotherapy, and pain medications.

Intervention

The intervention group was provided access to an online predictive tool at the beginning of the study. This tool offered information regarding the likelihood of improvement in quality of life if patients chose to undergo TKA. The predictions were based on the patient's age, sex, and baseline symptoms. Conversely, the control group received TAU without access to the predictive tool.

Main Outcomes and Measures

The primary outcome measure was the reduction in participants' willingness to undergo surgery at 6 months after tool use as measured by binomial logistic regression. Secondary outcome measures included participant treatment preference and the quality of their decision-making process as measured by the Knee Decision Quality Instrument.

Results

Of 211 randomized participants (mean [SD] age, 65.8 [8.3] years; 118 female [55.9%]), 105 were allocated to the predictive tool group and 106 to the TAU group. After adjusting for baseline differences in willingness for surgery, the predictive tool did not significantly reduce the primary outcome of willingness for surgery at 6 months (adjusted odds ratio, 0.85; 95% CI, 0.42-1.71; P = .64).

Conclusions and Relevance

Despite the absence of treatment effect on willingness for TKA, predictive tools might still enhance health outcomes of patients with knee osteoarthritis. Additional research is needed to optimize the design and implementation of predictive tools, address limitations, and fully understand their effect on the decision-making process in TKA.

Trial Registration

ANZCTR.org.au Identifier: ACTRN12622000072718.

Unveiling the potential of artificial intelligence in orthopaedic surgery

Artificial intelligence is paving the way in contemporary medical advances, with the potential to revolutionise orthopaedic surgical care. By harnessing the power of complex algorithms, artificial intelligence yields outputs that have diverse applications including, but not limited to, identifying implants, diagnostic imaging for fracture and tumour recognition, prognostic tools through the use of electronic medical records, assessing arthroplasty outcomes, length of hospital stay and economic costs, monitoring the progress of functional rehabilitation, and innovative surgical training via simulation. However, amid the promising potential and enthusiasm surrounding artificial intelligence, clinicians should understand its limitations, and caution is needed before artificial intelligence-driven tools are introduced to clinical practice.

Artificial intelligence in orthopaedic surgery

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as 'big data', AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI's limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.

Editorial Commentary: Machine Learning and Artificial Intelligence Are Tools Requiring Physician and Patient Input When Screening Patients at Risk for Extended, Postoperative Opioid Use

As the implementation of artificial intelligence in orthopedic surgery research flourishes, so grows the need for responsible use. Related research requires clear reporting of algorithmic error rates. Recent studies show that preoperative opioid use, male sex, and greater body mass index are risk factors for extended, postoperative opioid use, but may result in high false positive rates. Thus, to be applied clinically when screening patients, these tools require physician and patient input, and nuanced interpretation, as the utility of these screening tools diminish without providers interpreting and acting on the information. Machine learning and artificial intelligence should be viewed as tools that can facilitate these human conversations among patients, orthopedic surgeons, and health care providers.

Camouflage uncovered: what should happen next?

Recent publications have drawn attention to the fact that some brands of joint replacement may contain variants which perform significantly worse (or better) than their 'siblings'. As a result, the National Joint Registry has performed much more detailed analysis on the larger families of knee arthroplasties in order to identify exactly where these differences may be present and may hitherto have remained hidden. The analysis of the Nexgen knee arthroplasty brand identified that some posterior-stabilized combinations have particularly high revision rates for aseptic loosening of the tibia, and consequently a medical device recall has been issued for the Nexgen 'option' tibial component which was implicated. More elaborate signal detection is required in order to identify such variation in results in a routine fashion if patients are to be protected from such variation in outcomes between closely related implant types.

Profiling the Immune Response to Periprosthetic Joint Infection and Non-Infectious Arthroplasty Failure

Arthroplasty failure is a major complication of joint replacement surgery. It can be caused by periprosthetic joint infection (PJI) or non-infectious etiologies, and often requires surgical intervention and (in select scenarios) resection and reimplantation of implanted devices. Fast and accurate diagnosis of PJI and non-infectious arthroplasty failure (NIAF) is critical to direct medical and surgical treatment; differentiation of PJI from NIAF may, however, be unclear in some cases. Traditional culture, nucleic acid amplification tests, metagenomic, and metatranscriptomic techniques for microbial detection have had success in differentiating the two entities, although microbiologically negative apparent PJI remains a challenge. Single host biomarkers or, alternatively, more advanced immune response profiling-based approaches may be applied to differentiate PJI from NIAF, overcoming limitations of microbial-based detection methods and possibly, especially with newer approaches, augmenting them. In this review, current approaches to arthroplasty failure diagnosis are briefly overviewed, followed by a review of host-based approaches for differentiation of PJI from NIAF, including exciting futuristic combinational multi-omics methodologies that may both detect pathogens and assess biological responses, illuminating causes of arthroplasty failure.