Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner's hype cycle

A Scientometric Worldview of Artificial Intelligence in Musculoskeletal Diseases Since the 21st Century

Purpose

Over the past 24 years, significant advancements have been made in applying artificial intelligence (AI) to musculoskeletal (MSK) diseases. However, there is a lack of analytical and descriptive investigations on the trajectory, essential research directions, current research scenario, pivotal focuses, and future perspectives. This research aims to provide a thorough update on the progress in AI for MSK diseases over the last 24 years.

Methods

Data from the Web of Science database, covering January 1, 2000, to March 1, 2024, was analyzed. Using advanced analytical tools, we conducted comprehensive scientometric and visual analyses.

Results

The findings highlight the predominant influence of the USA, which accounts for 28.53% of the total publications and plays a key role in shaping research in this field. Notable productivity was seen at institutions such as the University of California, San Francisco, Harvard Medical School, and Seoul National University. Valentina Pedoia is identified as the most prolific contributor. Scientific Reports had the highest number of publications in this area. The five most significant diseases are joint diseases, bone fractures, bone tumors, cartilage diseases, and spondylitis.

Conclusion

This comprehensive scientometric assessment benefits both experienced researchers and newcomers, providing quick access to essential information and fostering the development of innovative concepts in this field.

Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review

PURPOSE

This systematic review aimed to investigate the reliability of AI predictive models of intraoperative implant sizing in total knee arthroplasty (TKA).

METHODS

Four databases were searched from inception till July 2023 for original studies that studied the reliability of AI prediction in TKA. The primary outcome was the accuracy ± 1 size. This review was conducted per PRISMA guidelines, and the risk of bias was assessed using the MINORS criteria.

RESULTS

A total of four observational studies comprised of at least 34,547 patients were included in this review. A mean MINORS score of 11 out of 16 was assigned to the review. All included studies were published between 2021 and 2022, with a total of nine different AI algorithms reported. Among these AI models, the accuracy of TKA femoral component sizing prediction ranged from 88.3 to 99.7% within a deviation of one size, while tibial component sizing exhibited an accuracy ranging from 90 to 99.9% ± 1 size.

CONCLUSION

This study demonstrated the potential of AI as a valuable complement for planning TKA, exhibiting a satisfactory level of reliability in predicting TKA implant sizes. This predictive accuracy is comparable to that of the manual and digital templating techniques currently documented in the literature. However, future research is imperative to assess the impact of AI on patient care and cost-effectiveness.

LEVEL OF EVIDENCE III

PROSPERO registration number: CRD42023446868.

Systematic review of machine-learning models in orthopaedic trauma

Aims

Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.

Methods

A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.

Results

A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures.

Conclusion

The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice.

How AI May Transform Musculoskeletal Imaging

While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.

[Application and prospect of machine learning in orthopaedic trauma]

Objective

To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice.

Methods

A comprehensive literature review was conducted to assess the status of machine learning algorithms in orthopaedic trauma research, both nationally and internationally.

Results

The rapid advancement of computer data processing and the growing convergence of medicine and industry have led to the widespread utilization of artificial intelligence in healthcare. Currently, machine learning plays a significant role in orthopaedic trauma, demonstrating high performance and accuracy in various areas including fracture image recognition, diagnosis stratification, clinical decision-making, evaluation, perioperative considerations, and prognostic risk prediction. Nevertheless, challenges persist in the development and clinical implementation of machine learning. These include limited database samples, model interpretation difficulties, and universality and individualisation variations.

Conclusion

The expansion of clinical sample sizes and enhancements in algorithm performance hold significant promise for the extensive application of machine learning in supporting orthopaedic trauma diagnosis, guiding decision-making, devising individualized medical strategies, and optimizing the allocation of clinical resources.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

 Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

 The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

 AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

  · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

Artificial intelligence in orthopedics: A qualitative exploration of the surgeon perspective

Artificial intelligence (AI) is currently integrated into many medical services. AI is utilized in many aspects of orthopedic surgery. The scope ranges from diagnosis to complex surgery. To evaluate the perceptions, attitudes, and interests of Sudanese orthopedic surgeons regarding the different applications of AI in orthopedic surgery. This qualitative questionnaire-based study was conducted through an anonymous electronic survey using Google Forms distributed among Sudanese orthopedic surgeons. The questionnaire entailed 4 sections. The first section included the participants' demographic data. The remaining 3 sections included questions for the assessment of the perception, attitude, and interest of surgeons toward (AI). The validity and reliability of the questionnaire were tested and piloted before the final dissemination. One hundred twenty-nine surgeons responded to the questionnaires. Most respondents needed to be more aware of the basic concepts of AI. However, most respondents were aware of its use in spinal and joint replacement surgeries. Most respondents had doubts regarding the safety of (AI). However, they were highly interested in utilizing (AI) in many orthopedic surgical aspects. Orthopedic surgery is a rapidly evolving branch of surgery that involves adoption of new technologies. Therefore, orthopedic surgeons should be encouraged to enroll in research activities to generate more studies and reviews to assess the usefulness and safety of emerging technologies.

Predicting five-year mortality in soft-tissue sarcoma patients

Aims

The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients.

Methods

Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset.

Results

A total of 13,646 patients with STS from the SEER database were included, of whom 35.9% experienced five-year cancer-related mortality. The random forest model performed the best overall and identified tumour size as the most important variable when predicting mortality in patients with STS, followed by M stage, histological subtype, age, and surgical excision. Each variable was significant in logistic regression. External validation yielded an AUC of 0.752.

Conclusion

This study identified clinically important variables associated with five-year cancer-related mortality in patients with limb and trunk STS, and developed a predictive model that demonstrated good accuracy and predictability. Orthopaedic oncologists may use these findings to further risk-stratify their patients and recommend an optimal course of treatment.

Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials

To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration). The developed algorithms distinguished between patients at high and low risk for 90-day and one-year mortality. The penalized logistic regression algorithm had the best performance metrics for both 90-day (c-statistic 0.80, calibration slope 0.95, calibration intercept -0.06, and Brier score 0.039) and one-year (c-statistic 0.76, calibration slope 0.86, calibration intercept -0.20, and Brier score 0.074) mortality prediction in the hold-out set. Using high-quality data, the ML-based prediction models accurately predicted 90-day and one-year mortality in patients aged 50 years or older with a FNF. The final models must be externally validated to assess generalizability to other populations, and prospectively evaluated in the process of shared decision-making.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

· Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

CITATION FORMAT

· Feuerecker B, Heimer M, Geyer T et al. Artificial Intelligence in Oncological Hybrid Imaging. Fortschr Röntgenstr 2023; 195: 105 - 114.

3D printed patient-specific prostate cancer models to guide nerve-sparing robot-assisted radical prostatectomy: a systematic review

Precise knowledge of each patient's index cancer and surrounding anatomy is required for nerve-sparing robot-assisted radical prostatectomy (NS-RARP). Complementary to this, 3D printing has proven its utility in improving the visualisation of complex anatomy. This is the first systematic review to critically assess the potential of 3D printed patient-specific prostate cancer models in improving visualisation and the practice of NS-RARP. A literature search of PubMed and OVID Medline databases was performed using the terms "3D Printing", "Robot Assisted Radical Prostatectomy" and related index terms as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eight articles were included; six were identified via database searches, to which a further two articles were located via a snowballing approach. Eight papers were identified for review. There were five prospective single centre studies, one case series, one technical report and one letter to the editor. Of these articles, five publications (62.5%) reported on the utility of 3D printed models for NS-RARP planning. Two publications (25%) utilised 3D printed prostate models for simulation and training, and two publications (25%) used the models for patient engagement. Despite the nascency of the field, 3D printed models are emerging in the uro-oncological literature as a useful tool in visualising complex anatomy. This has proven useful in NS-RARP for preoperative planning, simulation and patient engagement. However, best practice guidelines, the future regulatory landscape, and health economic considerations need to be addressed before this synergy of new technologies is ready for the mainstream.

The medical profession transformed by artificial intelligence: Qualitative study

BACKGROUND

Healthcaare delivery will change through the increasing use of artificial intelligence (AI). Physicians are likely to be among the professions most affected, though to what extent is not yet clear.

OBJECTIVE

We analyzed physicians' and AI experts' stances towards AI-induced changes. This concerned (1) physicians' tasks, (2) job replacement risk, and (3) implications for the ways of working, including human-AI interaction, changes in job profiles, and hierarchical and cross-professional collaboration patterns.

METHODS

We adopted an exploratory, qualitative research approach, using semi-structured interviews with 24 experts in the fields of AI and medicine, medical informatics, digital medicine, and medical education and training. Thematic analysis of the interview transcripts was performed.

RESULTS

Specialized tasks currently performed by physicians in all areas of medicine would likely be taken over by AI, including bureaucratic tasks, clinical decision support, and research. However, the concern that physicians will be replaced by an AI system is unfounded, according to experts; AI systems today would be designed only for a specific use case and could not replace the human factor in the patient-physician relationship. Nevertheless, the job profile and professional role of physicians would be transformed as a result of new forms of human-AI collaboration and shifts to higher-value activities. AI could spur novel, more interprofessional teams in medical practice and research and, eventually, democratization and de-hierarchization.

CONCLUSIONS

The study highlights changes in job profiles of physicians and outlines demands for new categories of medical professionals considering AI-induced changes of work. Physicians should redefine their self-image and assume more responsibility in the age of AI-supported medicine. There is a need for the development of scenarios and concepts for future job profiles in the health professions as well as their education and training.

Patients With Femoral Neck Fractures Are at Risk for Conversion to Arthroplasty After Internal Fixation: A Machine-learning Algorithm

BACKGROUND

Femoral neck fractures are common and are frequently treated with internal fixation. A major disadvantage of internal fixation is the substantially high number of conversions to arthroplasty because of nonunion, malunion, avascular necrosis, or implant failure. A clinical prediction model identifying patients at high risk of conversion to arthroplasty may help clinicians in selecting patients who could have benefited from arthroplasty initially.

QUESTION/PURPOSE

What is the predictive performance of a machine-learning (ML) algorithm to predict conversion to arthroplasty within 24 months after internal fixation in patients with femoral neck fractures?

METHODS

We included 875 patients from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial. The FAITH trial consisted of patients with low-energy femoral neck fractures who were randomly assigned to receive a sliding hip screw or cancellous screws for internal fixation. Of these patients, 18% (155 of 875) underwent conversion to THA or hemiarthroplasty within the first 24 months. All patients were randomly divided into a training set (80%) and test set (20%). First, we identified 27 potential patient and fracture characteristics that may have been associated with our primary outcome, based on biomechanical rationale and previous studies. Then, random forest algorithms (an ML learning, decision tree-based algorithm that selects variables) identified 10 predictors of conversion: BMI, cardiac disease, Garden classification, use of cardiac medication, use of pulmonary medication, age, lung disease, osteoarthritis, sex, and the level of the fracture line. Based on these variables, five different ML algorithms were trained to identify patterns related to conversion. The predictive performance of these trained ML algorithms was assessed on the training and test sets based on the following performance measures: (1) discrimination (the model's ability to distinguish patients who had conversion from those who did not; expressed with the area under the receiver operating characteristic curve [AUC]), (2) calibration (the plotted estimated versus the observed probabilities; expressed with the calibration curve intercept and slope), and (3) the overall model performance (Brier score: a composite of discrimination and calibration).

RESULTS

None of the five ML algorithms performed well in predicting conversion to arthroplasty in the training set and the test set; AUCs of the algorithms in the training set ranged from 0.57 to 0.64, slopes of calibration plots ranged from 0.53 to 0.82, calibration intercepts ranged from -0.04 to 0.05, and Brier scores ranged from 0.14 to 0.15. The algorithms were further evaluated in the test set; AUCs ranged from 0.49 to 0.73, calibration slopes ranged from 0.17 to 1.29, calibration intercepts ranged from -1.28 to 0.34, and Brier scores ranged from 0.13 to 0.15.

CONCLUSION

The predictive performance of the trained algorithms was poor, despite the use of one of the best datasets available worldwide on this subject. If the current dataset consisted of different variables or more patients, the performance may have been better. Also, various reasons for conversion to arthroplasty were pooled in this study, but the separate prediction of underlying pathology (such as, avascular necrosis or nonunion) may be more precise. Finally, it may be possible that it is inherently difficult to predict conversion to arthroplasty based on preoperative variables alone. Therefore, future studies should aim to include more variables and to differentiate between the various reasons for arthroplasty.

LEVEL OF EVIDENCE

Level III, prognostic study.

Development and external validation of automated detection, classification, and localization of ankle fractures: inside the black box of a convolutional neural network (CNN)

Purpose

Convolutional neural networks (CNNs) are increasingly being developed for automated fracture detection in orthopaedic trauma surgery. Studies to date, however, are limited to providing classification based on the entire image—and only produce heatmaps for approximate fracture localization instead of delineating exact fracture morphology. Therefore, we aimed to answer (1) what is the performance of a CNN that detects, classifies, localizes, and segments an ankle fracture, and (2) would this be externally valid?

Methods

The training set included 326 isolated fibula fractures and 423 non-fracture radiographs. The Detectron2 implementation of the Mask R-CNN was trained with labelled and annotated radiographs. The internal validation (or ‘test set’) and external validation sets consisted of 300 and 334 radiographs, respectively. Consensus agreement between three experienced fellowship-trained trauma surgeons was defined as the ground truth label. Diagnostic accuracy and area under the receiver operator characteristic curve (AUC) were used to assess classification performance. The Intersection over Union (IoU) was used to quantify accuracy of the segmentation predictions by the CNN, where a value of 0.5 is generally considered an adequate segmentation.

Results

The final CNN was able to classify fibula fractures according to four classes (Danis-Weber A, B, C and No Fracture) with AUC values ranging from 0.93 to 0.99. Diagnostic accuracy was 89% on the test set with average sensitivity of 89% and specificity of 96%. External validity was 89–90% accurate on a set of radiographs from a different hospital. Accuracies/AUCs observed were 100/0.99 for the ‘No Fracture’ class, 92/0.99 for ‘Weber B’, 88/0.93 for ‘Weber C’, and 76/0.97 for ‘Weber A’. For the fracture bounding box prediction by the CNN, a mean IoU of 0.65 (SD ± 0.16) was observed. The fracture segmentation predictions by the CNN resulted in a mean IoU of 0.47 (SD ± 0.17).

Conclusions

This study presents a look into the ‘black box’ of CNNs and represents the first automated delineation (segmentation) of fracture lines on (ankle) radiographs. The AUC values presented in this paper indicate good discriminatory capability of the CNN and substantiate further study of CNNs in detecting and classifying ankle fractures.

Level of evidence

II, Diagnostic imaging study.

Artificial Intelligence in Modern Orthopaedics: Current and Future Applications

➢

With increasing computing power, artificial intelligence (AI) has gained traction in all aspects of health care delivery. Orthopaedics is no exception because the influence of AI technology has become intricately linked with its advancement as evidenced by increasing interest and research.

➢

This review is written for the orthopaedic surgeon to develop a better understanding of the main clinical applications and potential benefits of AI within their day-to-day practice.

➢

A brief and easy-to-understand foundation for what AI is and the different terminology used within the literature is first provided, followed by a summary of the newest research on AI applications demonstrating increased accuracy and convenience in risk stratification, clinical decision-making support, and robotically assisted surgery.

The Challenges of Patient Selection for Prostate Cancer Focal Therapy: A Retrospective Observational Multicentre Study

Increased diagnoses of silent prostate cancer (PCa) have led to overtreatment and consequent functional side effects. Focal therapy (FT) applies energy to a prostatic index lesion treating only the clinically significant PCa focus. We analysed the potential predictive factors of FT failure. We collected data from patients who underwent robot-assisted radical prostatectomy (RARP) in two high-volume hospitals from January 2017 to January 2020. The inclusion criteria were: one MRI-detected lesion with a Gleason Score (GS) of ≤7, ≤cT2a, PSA of ≤10 ng/mL, and GS 6 on a random biopsy with ≤2 positive foci out of 12. Potential oncological safety of FT was defined as the respect of clinicopathological inclusion criteria on histology specimens, no extracapsular extension, and no biochemical, local, or metastatic recurrence within 12 months. To predict FT failure, we performed uni- and multivariate logistic regression. Sixty-seven patients were enrolled. The MRI index lesion median size was 11 mm; target lesions were ISUP grade 1 in 27 patients and ISUP grade 2 in 40. Potential FT failure occurred in 32 patients, and only the PSA value resulted as a predictive parameter (p < 0.05). The main issue for FT is patient selection, mainly because of multifocal csPCa foci. Nevertheless, FT could represent a therapeutic alternative for highly selected low-risk PCa patients.

Artificial intelligence and computer vision in orthopaedic trauma : the why, what, and how

Artificial intelligence (AI) is, in essence, the concept of 'computer thinking', encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the 'why'), the current applications (the 'what'), and the approach to unlocking its full potential (the 'how'). Cite this article: Bone Joint J 2022;104-B(8):911-914.

A Machine Learning Algorithm to Estimate the Probability of a True Scaphoid Fracture After Wrist Trauma

PURPOSE

To identify predictors of a true scaphoid fracture among patients with radial wrist pain following acute trauma, train 5 machine learning (ML) algorithms in predicting scaphoid fracture probability, and design a decision rule to initiate advanced imaging in high-risk patients.

METHODS

Two prospective cohorts including 422 patients with radial wrist pain following wrist trauma were combined. There were 117 scaphoid fractures (28%) confirmed on computed tomography, magnetic resonance imaging, or radiographs. Eighteen fractures (15%) were occult. Predictors of a scaphoid fracture were identified among demographics, mechanism of injury and examination maneuvers. Five ML-algorithms were trained in calculating scaphoid fracture probability. ML-algorithms were assessed on ability to discriminate between patients with and without a fracture (area under the receiver operating characteristic curve), agreement between observed and predicted probabilities (calibration), and overall performance (Brier score). The best performing ML-algorithm was incorporated into a probability calculator. A decision rule was proposed to initiate advanced imaging among patients with negative radiographs.

RESULTS

Pain over the scaphoid on ulnar deviation, sex, age, and mechanism of injury were most strongly associated with a true scaphoid fracture. The best performing ML-algorithm yielded an area under the receiver operating characteristic curve, calibration slope, intercept, and Brier score of 0.77, 0.84, -0.01 and 0.159, respectively. The ML-derived decision rule proposes to initiate advanced imaging in patients with radial-sided wrist pain, negative radiographs, and a fracture probability of ≥10%. When applied to our cohort, this would yield 100% sensitivity, 38% specificity, and would have reduced the number of patients undergoing advanced imaging by 36% without missing a fracture.

CONCLUSIONS

The ML-algorithm accurately calculated scaphoid fracture probability based on scaphoid pain on ulnar deviation, sex, age, and mechanism of injury. The ML-decision rule may reduce the number of patients undergoing advanced imaging by a third with a small risk of missing a fracture. External validation is required before implementation.

TYPE OF STUDY/LEVEL OF EVIDENCE

Diagnostic II.

Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above

Purpose

Preoperative prediction of mortality in femoral neck fracture patients aged 65 years or above may be valuable in the treatment decision-making. A preoperative clinical prediction model can aid surgeons and patients in the shared decision-making process, and optimize care for elderly femoral neck fracture patients. This study aimed to develop and internally validate a clinical prediction model using machine learning (ML) algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above.

Methods

A retrospective cohort study at two trauma level I centers and three (non-level I) community hospitals was conducted to identify patients undergoing surgical fixation for a femoral neck fracture. Five different ML algorithms were developed and internally validated and assessed by discrimination, calibration, Brier score and decision curve analysis.

Results

In total, 2478 patients were included with 90 day and 2 year mortality rates of 9.1% (n = 225) and 23.5% (n = 582) respectively. The models included patient characteristics, comorbidities and laboratory values. The stochastic gradient boosting algorithm had the best performance for 90 day mortality prediction, with good discrimination (c-statistic = 0.74), calibration (intercept = − 0.05, slope = 1.11) and Brier score (0.078). The elastic-net penalized logistic regression algorithm had the best performance for 2 year mortality prediction, with good discrimination (c-statistic = 0.70), calibration (intercept = − 0.03, slope = 0.89) and Brier score (0.16). The models were incorporated into a freely available web-based application, including individual patient explanations for interpretation of the model to understand the reasoning how the model made a certain prediction: https://sorg-apps.shinyapps.io/hipfracturemortality/

Conclusions

The clinical prediction models show promise in estimating mortality prediction in elderly femoral neck fracture patients. External and prospective validation of the models may improve surgeon ability when faced with the treatment decision-making.

Level of evidence

Prognostic Level II.

Alternative- and focal therapy trends for prostate cancer: a total population analysis of in-patient treatments in Germany from 2006 to 2019

Purpose

Focal therapy (FT) offers an alternative approach for prostate cancer (PCa) treatment in selected patients. However, little is known on its actual establishment in health care reality.

Patients and methods

We defined FT as high-intensity focused ultrasound (HIFU), hyperthermia ablation, cryotherapy, transurethral ultrasound ablation (TULSA) or vascular-targeted photodynamic therapy (VTP) TOOKAD^®. We analyzed the nationwide German hospital billing database for a PCa diagnosis in combination with FT. For analyses on the hospital level, we used the reimbursement.INFO tool based on hospitals’ quality reports. The study period was 2006 to 2019.

Results

We identified 23,677 cases of FT from 2006 to 2019. Considering all PCa cases with surgery, radiotherapy or FT, the share of FT was stable at 4%. The annual caseload of FT increased to a maximum of 2653 cases in 2008 (p < 0.001) and then decreased to 1182 cases in 2014 (p < 0.001). Since 2015, the cases of FT remained on a plateau around 1400 cases per year. The share of HIFU was stable at 92–96% from 2006 to 2017 and decreased thereafter to 75% in 2019 (p = 0.015). In 2019, VTP-TOOKAD^® increased to 11.5% and TULSA to 6%. In 2006, 21% (62/299) of urological departments performed FT and 20 departments reached > 20 FT procedures. In 2019, 16% (58/368) of urological departments performed FT and 7 departments reached > 20 FT. In 2019, 25 urological departments offered FT other than HIFU: 5 centers hyperthermia ablation, 11 centers VTP TOOKAD^®, 3 centers cryotherapy, 6 centers TULSA.

Conclusion

The FT development in Germany followed the Gartner hype cycle. While HIFU treatment is the most commonly performed FT, the share of newer FT modalities such as VTP-TOOKAD^® and TULSA is remarkably increasing.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00345-022-04024-0.

Artificial intelligence in orthopaedic surgery: transforming technological innovation in patient care and surgical training

Artificial intelligence (AI) is an exciting field combining computer science with robust data sets to facilitate problem-solving. It has the potential to transform education, practice and delivery of healthcare especially in orthopaedics. This review article outlines some of the already used AI pathways as well as recent technological advances in orthopaedics. Additionally, this article further explains how potentially these two entities could be combined in the future to improve surgical education, training and ultimately patient care and outcomes.

In Silico Finite Element Modeling of Stress Distribution in Osteosynthesis after Pertrochanteric Fractures

A stabilization method of pertrochanteric femur fractures is a contentious issue. Here, we assess the feasibility of rapid in silico 2D finite element modeling (FEM) to predict the distribution of stresses arising during the two most often used stabilization methods: gamma nail fixation (GNF) and dynamic hip screw (DHS). The modeling was based on standard pre-surgery radiographs of hip joints of 15 patients with pertrochanteric fractures of type A1, A2, and A3 according to the AO/OTA classification. The FEM showed that the stresses were similar for both GNF and DHS, with the medians ranging between 53-60 MPa and consistently lower for A1 than A3 fractures. Stresses also appeared in the fixation materials being about two-fold higher for GNF. Given similar bone stresses caused by both GNF and DHS but shorter surgery time, less extensive dissection, and faster patient mobilization, we submit that the GNF stabilization appears to be the most optimal system for pertrochanteric fractures. In silico FEM appears a viable perioperative method that helps predict the distribution of compressive stresses after osteosynthesis of pertrochanteric fractures. The promptness of modeling fits well into the rigid time framework of hip fracture surgery and may help optimize the fixation procedure for the best outcome. The study extends the use of FEM in complex orthopedic management. However, further datasets are required to firmly position the FEM in the treatment of pertrochanteric fractures.

Feasibility of Machine Learning and Logistic Regression Algorithms to Predict Outcome in Orthopaedic Trauma Surgery

BACKGROUND

Statistical models using machine learning (ML) have the potential for more accurate estimates of the probability of binary events than logistic regression. The present study used existing data sets from large musculoskeletal trauma trials to address the following study questions: (1) Do ML models produce better probability estimates than logistic regression models? (2) Are ML models influenced by different variables than logistic regression models?

METHODS

We created ML and logistic regression models that estimated the probability of a specific fracture (posterior malleolar involvement in distal spiral tibial shaft and ankle fractures, scaphoid fracture, and distal radial fracture) or adverse event (subsequent surgery [after distal biceps repair or tibial shaft fracture], surgical site infection, and postoperative delirium) using 9 data sets from published musculoskeletal trauma studies. Each data set was split into training (80%) and test (20%) subsets. Fivefold cross-validation of the training set was used to develop the ML models. The best-performing model was then assessed in the independent testing data. Performance was assessed by (1) discrimination (c-statistic), (2) calibration (slope and intercept), and (3) overall performance (Brier score).

RESULTS

The mean c-statistic was 0.01 higher for the logistic regression models compared with the best ML models for each data set (range, -0.01 to 0.06). There were fewer variables strongly associated with variation in the ML models, and many were dissimilar from those in the logistic regression models.

CONCLUSIONS

The observation that ML models produce probability estimates comparable with logistic regression models for binary events in musculoskeletal trauma suggests that their benefit may be limited in this context.

AI MSK clinical applications: spine imaging

Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.

Interpretation and reporting of predictive or diagnostic machine-learning research in Trauma & Orthopaedics

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines. Cite this article: Bone Joint J 2021;103-B(12):1754-1758.

An increasing number of convolutional neural networks for fracture recognition and classification in orthopaedics : are these externally validated and ready for clinical application?

Aims

The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs?

Methods

The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and diagnostic performance characteristics on the IV and EV datasets were collected and compared. Quality assessment was conducted using a seven-item checklist based on a modified Methodologic Index for NOn-Randomized Studies instrument (MINORS).

Results

Out of 1,349 studies, 36 reported development of a CNN for fracture detection and/or classification. Of these, only four (11%) reported a form of EV. One study used temporal EV, one conducted both temporal and geographical EV, and two used geographical EV. When comparing the CNN’s performance on the IV set versus the EV set, the following were found: AUCs of 0.967 (IV) versus 0.975 (EV), 0.976 (IV) versus 0.985 to 0.992 (EV), 0.93 to 0.96 (IV) versus 0.80 to 0.89 (EV), and F1-scores of 0.856 to 0.863 (IV) versus 0.757 to 0.840 (EV).

Conclusion

The number of externally validated CNNs in orthopaedic trauma for fracture recognition is still scarce. This greatly limits the potential for transfer of these CNNs from the developing institute to another hospital to achieve similar diagnostic performance. We recommend the use of geographical EV and statements such as the Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI), the Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence (SPIRIT-AI) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis–Machine Learning (TRIPOD-ML) to critically appraise performance of CNNs and improve methodological rigor, quality of future models, and facilitate eventual implementation in clinical practice.

Cite this article: Bone Jt Open 2021;2(10):879–885.

Introduction to The Spine Journal special issue on artificial intelligence and machine learning

In the last 5 years, artificial intelligence (AI) algorithms have made rapid advances for diagnosis and prognosis in fields ranging from dermatology to anesthesiology. How do we make sense of the rise of AI in healthcare and specifically in spine? How much of what we see today is "hype" and what will remain when the dust settles? In this special issue, several reviews and original articles help us understand the state of AI in healthcare today, the avenues for future progress, and the implications for spine care.  Continued engagement, skepticism, and collaboration with technical experts will allow for the development of AI systems that complement and expand our abilities to diagnose, predict, and operate.

Machine learning in orthopaedic surgery

Artificial intelligence and machine learning in orthopaedic surgery has gained mass interest over the last decade or so. In prior studies, researchers have demonstrated that machine learning in orthopaedics can be used for different applications such as fracture detection, bone tumor diagnosis, detecting hip implant mechanical loosening, and grading osteoarthritis. As time goes on, the utility of artificial intelligence and machine learning algorithms, such as deep learning, continues to grow and expand in orthopaedic surgery. The purpose of this review is to provide an understanding of the concepts of machine learning and a background of current and future orthopaedic applications of machine learning in risk assessment, outcomes assessment, imaging, and basic science fields. In most cases, machine learning has proven to be just as effective, if not more effective, than prior methods such as logistic regression in assessment and prediction. With the help of deep learning algorithms, such as artificial neural networks and convolutional neural networks, artificial intelligence in orthopaedics has been able to improve diagnostic accuracy and speed, flag the most critical and urgent patients for immediate attention, reduce the amount of human error, reduce the strain on medical professionals, and improve care. Because machine learning has shown diagnostic and prognostic uses in orthopaedic surgery, physicians should continue to research these techniques and be trained to use these methods effectively in order to improve orthopaedic treatment.

Prediction of Shoulder Stiffness After Arthroscopic Rotator Cuff Repair

BACKGROUND

Postoperative shoulder stiffness (POSS) is a prevalent adverse event after arthroscopic rotator cuff repair (ARCR) that is associated with major limitations in everyday activities and prolonged rehabilitation.

PURPOSE/HYPOTHESIS

The purpose was to develop a predictive model for determining the risk of POSS within 6 months after primary ARCR. We hypothesized that sufficient discrimination ability of such a model could be achieved using a local institutional database.

STUDY DESIGN

Case-control study; Level of evidence, 3.

METHODS

Consecutive primary ARCRs documented in a local clinical registry between 2013 and 2017 were included, and patients who experienced POSS before the final clinical 6-month follow-up were identified. A total of 29 prognostic factor candidates were considered, including patient-related factors (n = 7), disease-related factors (n = 9), rotator cuff integrity factors (n = 6), and operative details (n = 7). We used imputed data for the primary analysis, and a sensitivity analysis was conducted using complete case data. Logistic regression was applied to develop a model based on clinical relevance and statistical criteria. To avoid overfitting in the multivariable model, highly correlated predictors were not included together in any model. A final prognostic model with a maximum of 8 prognostic factors was considered. The model's predictive accuracy was assessed by the area under the receiver operating characteristic curve (AUC). Internal validation was performed using bootstrapping.

RESULTS

Of 1330 ARCR cases (N = 1330 patients), 112 (8.4%) patients had POSS. Our final model had a moderate predictive ability with an AUC of 0.67. The predicted risks of POSS ranged from 2.3% to 38.9% and were significantly higher in women; patients with partial tears, low baseline passive shoulder abduction, and lack of tendon degeneration; and when no acromioplasty was performed.

CONCLUSION

A prognostic model for POSS was developed for patients with ARCR, offering a personalized risk evaluation to support the future decision process for surgery and rehabilitation.

Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.