Artificial intelligence in orthopedic surgery: current state and future perspective

Sensitivity and specificity of machine learning and deep learning algorithms in the diagnosis of thoracolumbar injuries resulting in vertebral fractures: A systematic review and meta-analysis

Introduction

Clinicians encounter challenges in promptly diagnosing thoracolumbar injuries (TLIs) and fractures (VFs), motivating the exploration of Artificial Intelligence (AI) and Machine Learning (ML) and Deep Learning (DL) technologies to enhance diagnostic capabilities. Despite varying evidence, the noteworthy transformative potential of AI in healthcare, leveraging insights from daily healthcare data, persists.

Research question

This review investigates the utilization of ML and DL in TLIs causing VFs.

Materials and methods

Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) methodology, a systematic review was conducted in PubMed and Scopus databases, identifying 793 studies. Seventeen were included in the systematic review, and 11 in the meta-analysis. Variables considered encompassed publication years, geographical location, study design, total participants (14,524), gender distribution, ML or DL methods, specific pathology, diagnostic modality, test analysis variables, validation details, and key study conclusions. Meta-analysis assessed specificity, sensitivity, and conducted hierarchical summary receiver operating characteristic curve (HSROC) analysis.

Results

Predominantly conducted in China (29.41%), the studies involved 14,524 participants. In the analysis, 11.76% (N = 2) focused on ML, while 88.24% (N = 15) were dedicated to deep DL. Meta-analysis revealed a sensitivity of 0.91 (95% CI = 0.86-0.95), consistent specificity of 0.90 (95% CI = 0.86-0.93), with a false positive rate of 0.097 (95% CI = 0.068-0.137).

Conclusion

The study underscores consistent specificity and sensitivity estimates, affirming the diagnostic test's robustness. However, the broader context of ML applications in TLIs emphasizes the critical need for standardization in methodologies to enhance clinical utility.

Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.

The Rise of Artificial Intelligence: Implications in Orthopedic Surgery

Artificial intelligence (AI) is slowly making its way into all domains and medicine is no exception. AI is already proving to be a promising tool in the health-care field. With respect to orthopedics, AI is already under use in diagnostics as in fracture and tumor detection, predictive algorithms to predict the mortality risk and duration of hospital stay or complications such as implant loosening and in real-time assessment of post-operative rehabilitation. AI could also be of use in surgical training, utilizing technologies such as virtual reality and augmented reality. However, clinicians should also be aware of the limitations of AI as validation is necessary to avoid errors. This article aims to provide a description of AI and its subfields, its current applications in orthopedics, the limitations, and its future prospects.

Future of Artificial Intelligence in Surgery: A Narrative Review

Artificial intelligence (AI) is the capability of a machine to execute cognitive processes that are typically considered to be functions of the human brain. It is the study of algorithms that enable machines to reason and perform mental tasks, including problem-solving, object and word recognition, and decision-making. Once considered science fiction, AI today is a fact and an increasingly prevalent subject in both academic and popular literature. It is expected to reshape medicine, benefiting both healthcare professionals and patients. Machine learning (ML) is a subset of AI that allows machines to learn and make predictions by recognizing patterns, thus empowering the medical team to deliver better care to patients through accurate diagnosis and treatment. ML is expanding its footprint in a variety of surgical specialties, including general surgery, ophthalmology, cardiothoracic surgery, and vascular surgery, to name a few. In recent years, we have seen AI make its way into the operating theatres. Though it has not yet been able to replace the surgeon, it has the potential to become a highly valuable surgical tool. Rest assured that the day is not far off when AI shall play a significant intraoperative role, a projection that is currently marred by safety concerns. This review aims to explore the present application of AI in various surgical disciplines and how it benefits both patients and physicians, as well as the current obstacles and limitations facing its seemingly unstoppable rise.

Automated decision support for Hallux Valgus treatment options using anteroposterior foot radiographs

BACKGROUND

Assessment of the potential utility of deep learning with subsequent image analysis to automate the measurement of hallux valgus and intermetatarsal angles from radiographs to serve as a preoperative aid in establishing hallux valgus severity for clinical decision-making.

AIM

To investigate the accuracy of automated measurements of angles of hallux valgus from radiographs for further integration with the preoperative planning process.

METHODS

The data comprises 265 consecutive digital anteroposterior weightbearing foot radiographs. 181 radiographs were utilized for training (161) and validating (20) a U-Net neural network to achieve a mean Sørensen–Dice index > 97% on bone segmentation. 84 test radiographs were used for manual (computer assisted) and automated measurements of hallux valgus severity determined by hallux valgus (HVA) and intermetatarsal angles (IMA). The reliability of manual and computer-based measurements was calculated using the interclass correlation coefficient (ICC) and standard error of measurement (SEM). Inter- and intraobserver reliability coefficients were also compared. An operative treatment recommendation was then applied to compare results between automated and manual angle measurements.

RESULTS

Very high reliability was achieved for HVA and IMA between the manual measurements of three independent clinicians. For HVA, the ICC between manual measurements was 0.96-0.99. For IMA, ICC was 0.78-0.95. Comparing manual against automated computer measurement, the reliability was high as well. For HVA, absolute agreement ICC and consistency ICC were 0.97, and SEM was 0.32. For IMA, absolute agreement ICC was 0.75, consistency ICC was 0.89, and SEM was 0.21. Additionally, a strong correlation (0.80) was observed between our approach and traditional clinical adjudication for preoperative planning of hallux valgus, according to an operative treatment algorithm proposed by EFORT.

CONCLUSION

The proposed automated, artificial intelligence assisted determination of hallux valgus angles based on deep learning holds great potential as an accurate and efficient tool, with comparable accuracy to manual measurements by expert clinicians. Our approach can be effectively implemented in clinical practice to determine the angles of hallux valgus from radiographs, classify the deformity severity, streamline preoperative decision-making prior to corrective surgery.

Application progress of artificial intelligence and augmented reality in orthopaedic arthroscopy surgery

In today's rapidly developing technological era, the technological revolution triggered by the rapid iteration of artificial intelligence and augmented reality has provided brand-new digital intelligent empowerment for orthopaedic clinical operation. Although traditional arthroscopy has been widely promoted globally due to its advantages such as minimally invasive, safety and early functional exercise, it still has deficiencies in precision and personalization. The assistance of artificial intelligence and augmented reality enables precise positioning and navigation in arthroscopic surgery, as well as personalized operations based on patient conditions, which lifts the objective limitations of traditional sports medicine surgery. The integration of artificial intelligence and augmented reality with orthopaedic arthroscopy surgery is still in infancy, even though there are still some insufficient to be solved, but its prospect is bright.

Transforming Orthopedic Joint Surgeries: The Role of Artificial Intelligence (AI) and Robotics

The landscape of orthopedic joint surgeries, specifically total hip arthroplasty (THA) and total knee arthroplasty (TKA), is rapidly changing, and artificial intelligence (AI) along with robotics is at the helm of this transformation. These technologies, working synergistically, have introduced unprecedented levels of precision and personalization to surgical procedures, thereby significantly enhancing patient outcomes. In this editorial, we explore the changing perspectives of orthopedic surgeons toward AI and robotics and dissect the incorporation of these technologies in surgeries, their associated advantages, their inherent limitations, and potential future prospects. We draw from a host of recent studies to provide a comprehensive understanding of how these transformative technologies can augment surgical performance and patient care.

Remote fit wrist braces through artificial intelligence

INTRODUCTION

Physical boundaries to access skilled orthotist or hand therapy care may be hindered by multiple factors, such as geography, or availability. This study evaluated the accuracy of fitting a prefabricated wrist splint using an app on a smart device. We hypothesize that remote brace fitting by artificial intelligence (AI) can accurately determine the brace size the patient needs without in-person fitting.

METHODS

Healthy volunteers were recruited to fit wrist braces. Using 2 standardized calibrated images captured by the smart device, each subject's image was loaded into the machine learning software (AI). Later, hand features were extracted, calibrated, and measured the application, calculated the correct splint size, and compared with the splint chosen by our subjects to improve its own accuracy. As a control (control 1), the subjects independently selected the best brace fit from an array of available splints. Subject selection was recorded and compared with the AI fit splint. As the second method of fitting (control 2), we compared the manufacturer recommended brace size (based on measured wrist circumference and provided sizing chart/insert brochure) with the AI fit splint.

RESULTS

A total of 54 volunteers were included. Thirty-two splints predicted by the algorithm matched the exact size chosen by each subject yielding 70% accuracy with a standard deviation of 10% ( p < 0.001). The accuracy increased to 90% with 5% standard deviation if the splints were predicted within the next size category. Fit by manufacturer sizing chart was only 33% in agreement with participant selection.

CONCLUSION

Remote brace fitting using AI prediction model may be an acceptable alternative to current standards because it can accurately predict wrist splint size. As more subjects were analyzed, the AI algorithm became more accurate predicting proper brace fit. In addition, AI fit braces are more than twice as accurate as relying on the manufacturer sizing chart.

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.

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 Corrosion Damage in the Human Body Using Artificial Intelligence: In Vitro Progress and Future Applications

Artificial intelligence (AI) is used in the clinic to improve patient care. While the successes illustrate AI's impact, few studies have led to improved clinical outcomes. In this review, we focus on how AI models implemented in nonorthopedic fields of corrosion science may apply to the study of orthopedic alloys. We first define and introduce fundamental AI concepts and models, as well as physiologically relevant corrosion damage modes. We then systematically review the corrosion/AI literature. Finally, we identify several AI models that may be implemented to study fretting, crevice, and pitting corrosion of titanium and cobalt chrome alloys.

Socioeconomic status influences participation in cancer prehabilitation and preparation for surgical recovery: A pooled retrospective analysis using a validated area-level socioeconomic status metric

BACKGROUND

Prehabilitation employs exercise, nutrition, and psychological interventions to optimize physiological status in preparation for surgery. First, we described the extent to which material deprivation index score (MDIS) influenced prehabilitation participation. Second, we evaluated the extent to which prehabilitation influenced recovery as compared to control.

METHODS

Pooled patient records from prospective multimodal prehabilitation studies in oncologic surgery were retrospectively examined. Patient postal codes were linked to their MDIS, a validated area-level socioeconomic status (SES) metric, as quintiles 1-5 (1 = highest SES). Functional capacity was evaluated with the 6-min walking test (6MWT) at baseline, before, and 8 weeks post-surgery. Influence of prehabilitation on length of hospital stay (LOS) was explored using generalized linear models with a negative binomial distribution adjusted for age, sex, surgical population, and MDIS.

RESULTS

Recruitment records were available from 2014 onwards, yielding 1013 eligible patients for prehabilitation participation with MDIS data. Fewer patients with a low SES enrolled (Q1:62% vs. Q5:47%; P = 0.01) and remained in prehabilitation studies (Q1: 59% vs. Q5: 45%; P = 0.07). Prehabilitation study records were available from 2008 onward, yielding 886 enrolled patients with MDIS data (n = 510 prehabilitation, n = 376 control). Preoperative 6MWT similarly improved by > 20 m in response to prehabilitation across SES strata (P < 0.05). Postoperative 6MWT could not be evaluated due to substantial missing data. Prehabilitation had a significant protective influence on LOS, as compared to control, in unadjusted and adjusted models [adjusted IRR:0.77 (95% CI:0.68 to 0.87; P < 0.001].

CONCLUSION

Findings suggest that prehabilitation is effective across all SES; however, participation across SES quintiles was not equal. Barriers to participation must be identified and addressed. Once these barriers are addressed, prehabilitation may reduce surgical disparities among SES.

Application of artificial intelligence in medical technologies: A systematic review of main trends

Objective

Artificial intelligence (AI) has been increasingly applied in various fields of science and technology. In line with the current research, medicine involves an increasing number of artificial intelligence technologies. The introduction of rapid AI can lead to positive and negative effects. This is a multilateral analytical literature review aimed at identifying the main branches and trends in the use of using artificial intelligence in medical technologies.

Methods

The total number of literature sources reviewed is n = 89, and they are analyzed based on the literature reporting evidence-based guideline PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for a systematic review.

Results

As a result, from the initially selected 198 references, 155 references were obtained from the databases and the remaining 43 sources were found on open internet as direct links to publications. Finally, 89 literature sources were evaluated after exclusion of unsuitable references based on the duplicated and generalized information without focusing on the users.

Conclusions

This article is identifying the current state of artificial intelligence in medicine and prospects for future use. The findings of this review will be useful for healthcare and AI professionals for improving the circulation and use of medical AI from design to implementation stage.

Industry 5.0 in Orthopaedics

Background

Industrial revolutions play a major role in the development of technologies in various fields. Currently, the world is marching towards softwarization and digitalization. There is an emerging need for conversion of Industry 4.0 to Industry 5.0 for technological development and implementation of the same in the digital era. In health care, digitalization emerged in Industry 4.0 revolution. To enhance patient care and quality of life, Industry 5.0 plays a major role in providing patient-centric care and customization and personalization of products. The integration of human intelligence with artificial intelligence provides a precise diagnosis and enhances the recovery and functional outcome of the patients.

Materials and methods

In this manuscript, the domains and limitations of Industry 5.0 and further research on Industry 6.0 were elaborated on to bring out technologies in better health care.

Results

Industry 5.0 lessens the work of medical professionals and integrates software-based diagnosis and management. It provides cost-effective manufacturing solutions with limited resources compared to Industry 4.0. Industry 5.0 focuses on SMART and additive manufacturing of implants, and the development of bio-scaffolds, prosthetics, and instruments. In this manuscript, the domains and limitations of Industry 5.0 and further research on Industry 6.0 were elaborated on to bring out technologies in better health care.

Conclusion

'The personalization and customization of products' are the hallmarks of this evolving Industry 5.0 revolution. The major uplifts in various domains of industry 5.0 such as advanced automation, digitalization, collaborative robots, and personalization bring this an inevitable mechano-scientific technological revolution in this current medical era.

Enhancing Orthopedic Surgery and Treatment Using Artificial Intelligence and Its Application in Health and Dietary Welfare

The current decade has seen an increased usage of high-end digital technologies like machine learning in the field of health care services which enable in supporting and performing different functions with less or no human interventions. The application of machine learning tools in the orthopedic area is gaining more popularity as it can support in analyzing the issues in a more comprehensive manner, provide accurate data, support in forecasting the pattern. It enables offering critical information for taking quick decisions by the medical practitioners in order to enhance the health and dietary care service delivery. The ML tools can support in collecting patient centric data related to orthopedic surgery and also estimate the postoperative complications, level of treatment modalities to be provided, and guide the medical practitioners in taking effective clinical device decisions. The ML approach also supports in providing prediction methods of implementing the ortho surgical outcomes. Furthermore, it can also guide in making better treatment procedures, forecast the patterns, and stream the health care management services for better patient recovery. This study implements a quantitative research approach which will support in sourcing the data from the respondents who are currently working as medical practitioners, orthopedic experts, and radiologists who use ML-based models in making critical decisions related to orthopedic surgery. The researchers chose nearly 149 respondents, and the information was analysed using the IBM SPSS package for gaining critical interpretation. The major analyses cover descriptive analysis, regression analysis, and analysis of variances.

Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Reverse Total Shoulder Arthroplasty

Background

Reverse total shoulder arthroplasty (rTSA) offers tremendous promise for the treatment of complex pathologies beyond the scope of anatomic total shoulder arthroplasty but is associated with a higher rate of major postoperative complications. We aimed to design and validate a machine learning (ML) model to predict major postoperative complications or readmission following rTSA.

Methods

We retrospectively reviewed California's Office of Statewide Health Planning and Development database for patients who underwent rTSA between 2015 and 2017. We implemented logistic regression (LR), extreme gradient boosting (XGBoost), gradient boosting machines, adaptive boosting, and random forest classifiers in Python and trained these models using 64 binary, continuous, and discrete variables to predict the occurrence of at least one major postoperative complication or readmission following primary rTSA. Models were validated using the standard metrics of area under the receiver operating characteristic (AUROC) curve, area under the precision–recall curve (AUPRC), and Brier scores. The key factors for the top-performing model were determined.

Results

Of 2799 rTSAs performed during the study period, 152 patients (5%) had at least 1 major postoperative complication or 30-day readmission. XGBoost had the highest AUROC and AUPRC of 0.681 and 0.129, respectively. The key predictive features in this model were patients with a history of implant complications, protein-calorie malnutrition, and a higher number of comorbidities.

Conclusion

Our study reports an ML model for the prediction of major complications or 30-day readmission following rTSA. XGBoost outperformed traditional LR models and also identified key predictive features of complications and readmission.

Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries

Background:

Technological innovation is a key component of orthopaedic surgery. With the integration of powerful technologies in surgery and clinical practice, artificial intelligence (AI) may become an important tool for orthopaedic surgeons in the future. Through adaptive learning and problem solving that serve to constantly increase accuracy, machine learning algorithms show great promise in orthopaedics.

Purpose:

To investigate the current and potential uses of AI in the management of anterior cruciate ligament (ACL) injury.

Study Design:

Systematic review; Level of evidence, 3.

Methods:

A systematic review of the PubMed, MEDLINE, Embase, Web of Science, and SPORTDiscus databases between their start and August 12, 2020, was performed by 2 independent reviewers. Inclusion criteria included application of AI anywhere along the spectrum of predicting, diagnosing, and managing ACL injuries. Exclusion criteria included non-English publications, conference abstracts, review articles, and meta-analyses. Statistical analysis could not be performed because of data heterogeneity; therefore, a descriptive analysis was undertaken.

Results:

A total of 19 publications were included after screening. Applications were divided based on the different stages of the clinical course in ACL injury: prediction (n = 2), diagnosis (n = 12), intraoperative application (n = 1), and postoperative care and rehabilitation (n = 4). AI-based technologies were used in a wide variety of applications, including image interpretation, automated chart review, assistance in the physical examination via optical tracking using infrared cameras or electromagnetic sensors, generation of predictive models, and optimization of postoperative care and rehabilitation.

Conclusion:

There is an increasing interest in AI among orthopaedic surgeons, as reflected by the applications for ACL injury presented in this review. Although some studies showed similar or better outcomes using AI compared with traditional techniques, many challenges need to be addressed before this technology is ready for widespread use.

Outpatient lower extremity fracture surgery: should we be concerned?

PURPOSE

With rising healthcare costs and insurance push against non-emergent hospital admission, lower extremity fracture treatment is shifting toward outpatient procedures over inpatient hospitalizations. This study compares outcomes for fractures treated as inpatient versus outpatient.

METHODS

We conducted a retrospective review of lower extremity fracture patients. We collected demographics, injury information, hospital course, and complication data. Length of stay was categorized as "inpatient" and "outpatient" based a 24-h hospital stay cutoff. Data analysis included differences between cohorts with regards to readmissions and complications.

RESULTS

We identified 229 patients who met inclusion criteria. Inpatient versus outpatient status was predictive of in-hospital complications; however, inpatient versus outpatient status did not predict 1-year readmission.

CONCLUSION

Outpatient surgery is safe and effective. As the population increases and ages, low-risk surgeries should be considered for outpatient rather than inpatient stays to lower costs, save resources, and reduce complications.

Artificial intelligence in musculoskeletal oncological radiology

BACKGROUND

Due to the rarity of primary bone tumors, precise radiologic diagnosis often requires an experienced musculoskeletal radiologist. In order to make the diagnosis more precise and to prevent the overlooking of potentially dangerous conditions, artificial intelligence has been continuously incorporated into medical practice in recent decades. This paper reviews some of the most promising systems developed, including those for diagnosis of primary and secondary bone tumors, breast, lung and colon neoplasms.

CONCLUSIONS

Although there is still a shortage of long-term studies confirming its benefits, there is probably a considerable potential for further development of computer-based expert systems aiming at a more efficient diagnosis of bone and soft tissue tumors.

Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol

BACKGROUND

Artificial intelligence (AI) and machine learning (ML) are interwoven into our everyday lives and have grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this systematic review will be to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives will be to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery.

METHODS

A systematic search will be conducted in PubMed, ScienceDirect, and Google Scholar databases for articles written in English, Italian, French, Spanish, and Portuguese language articles published up to September 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions and acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies and do not relate to orthopedic interventions or if no clinical data were produced. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and the results of meta-analyses will not be included in the analysis.

DISCUSSION

Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies and a smaller subset of articles describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed.

TRIAL REGISTRATION

The protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement.

Impact of industry 4.0 to create advancements in orthopaedics

Scientists and health professional are focusing on improving the medical sciences for the betterment of patients. The fourth industrial revolution, which is commonly known as Industry 4.0, is a significant advancement in the field of engineering. Industry 4.0 is opening a new opportunity for digital manufacturing with greater flexibility and operational performance. This development is also going to have a positive impact in the field of orthopaedics. The purpose of this paper is to present various advancements in orthopaedics by the implementation of Industry 4.0. To undertake this study, we have studied the available literature extensively on Industry 4.0, technologies of Industry 4.0 and their role in orthopaedics. Paper briefly explains about Industry 4.0, identifies and discusses the major technologies of Industry 4.0, which will support development in orthopaedics. Finally, from the available literature, the paper identifies twelve significant advancements of Industry 4.0 in orthopaedics. Industry 4.0 uses various types of digital manufacturing and information technologies to create orthopaedics implants, patient-specific tools, devices and innovative way of treatment. This revolution is to be useful to perform better spinal surgery, knee and hip replacement, and invasive surgeries.