Abstract
The twenty-first century has proven that data are the new gold. Artificial intelligence (AI) driven technologies might potentially change the clinical practice in all medical specialities, including orthopedic surgery. AI has a broad spectrum of subcomponents, including machine learning, which consists of a subdivision called deep learning. AI has the potential to increase healthcare delivery, improve indications and interventions, and minimize errors. In orthopedic surgery. AI supports the surgeon in the evaluation of radiological images, training of surgical residents, and excellent performance of machine-assisted surgery. The AI algorithms improve the administrative and management processes of hospitals and clinics, electronic healthcare databases, monitoring the outcomes, and safety controls. AI models are being developed in nearly all orthopedic subspecialties, including arthroscopy, arthroplasty, tumor, spinal and pediatric surgery. The present study discusses current applications, limitations, and future prospective of AI in foot and ankle surgery.
Zusammenfassung
Das 21. Jahrhundert hat bewiesen, dass Daten das neue Gold sind. Von künstlicher Intelligenz (KI) betriebene Technologien könnten die klinische Praxis in allen medizinischen Fachbereichen, einschließlich der orthopädischen Chirurgie, potenziell verändern. KI hat ein breites Spektrum von Teilkomponenten, darunter das maschinelle Lernen (Machine Learning), von welchem wiederum Deep Learning ein Teilbereich ist. KI hat das Potenzial, die Gesundheitsversorgung zu verbessern, Indikationen und Eingriffe zu optimieren sowie Fehler zu minimieren. KI unterstützt den Chirurgen bei der Auswertung radiologischer Bilder, bei der Ausbildung von Assistenzärzten in der Chirurgie und bei der exzellenten Durchführung maschinengestützter Operationen. Die KI-Algorithmen verbessern die Verwaltungs- und Managementprozesse von Krankenhäusern und Kliniken, elektronische Gesundheitsdatenbanken, die Überwachung der Ergebnisse und die Sicherheitskontrollen. KI-Modelle werden in fast allen orthopädischen Fachgebieten entwickelt, darunter Arthroskopie, Arthroplastik, Tumor‑, Wirbelsäulen- und Kinderchirurgie. Die vorliegende Studie erörtert aktuelle Anwendungen, Grenzen und Zukunftsaussichten der KI in der Fuß- und Sprunggelenkchirurgie.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
The twenty-first century has proven that data is the new gold. Artificial intelligence (AI) driven technologies might potentially change the clinical practice in all medical specialities, including orthopedic surgery. AI has a broad spectrum of subcomponents, including machine learning (ML), which consists of a subdivision called deep learning (DL). AI has the potential to increase healthcare delivery, improve indications and interventions, and minimize errors. AI is the intelligence demonstrated by the machines such as computers. It has several abilities such as a) to learn, b) to reason, c) to generalize, and d) to infer meaning. AI technology adapts and integrates several problem-solving techniques such as search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics. Presently, highly mathematical and statistical ML has dominated AI. These solve many challenging problems for academia. AI is the process of human-like intelligence simulated by using computer-controlled machines. It includes information, reasoning, and self-correction capability. AI is used with intelligent robots and the associated machinery to perform orthopedic surgery more accurately. These systems can detect mistakes in the given environment and provide actionable information regarding heat, light, movement, temperature, sound, and pressure and thus minimize human errors. In orthopedic surgery, AI supports the surgeon in the evaluation of radiological images, training of surgical residents and excellent performance of machine-assisted surgery. The AI algorithms improve the administrative and management processes of hospitals and clinics, electronic healthcare databases, monitoring the outcomes, and safety controls [1].
AI models are being developed in nearly all orthopedic subspecialties, including arthroscopy, arthroplasty, tumor, spinal and pediatric surgery. Klemt et al. developed and validated ML models to predict the risk of early revision following primary total hip arthroplasty (THA) [2]. Jo et al. proposed an ML model to predict the risk of blood transfusion following primary total knee arthroplasty (TKA) [3]. Merali et al. developed and validated a DL model for detecting cervical spinal cord compression in magnetic resonance imaging (MRI) scans [4]. Kunze et al. trained and tested several ML models for predicting patients who would achieve the minimal clinically important difference (MCID) in the hip outcome score-sports subscale (HOS-SS) following hip arthroscopy for femoroacetabular impingement syndrome [5]. Xu et al. developed a DL-assisted system for automated measurements and classifications pertinent to developmental dysplasia of the hip directly from plain pelvic radiographs [6].
Technological advances are happening at an accelerated speed and are being incorporated into healthcare. Several such technologies have made their way into orthopedics, such as computer navigation, robot-assisted arthroplasty and 3‑D planning. With the advent of progressive understanding and refinement of software algorithms, orthopedic surgery is now delving into AI systems. The present generations of AI algorithms help in image recognition and multivariate risk analysis, and outcome prediction. It is becoming obvious that AI and ML are likely to significantly impact clinical orthopedic practices in the short term, and will find newer applications, increased utility and the use of ML in clinical practice. AI is expected to provide solutions to the traditionally redundant and repetitive tasks that are lower on the intellectual spectrum and contribute to surgeons’ burnout and mistakes; however, AI faces several challenges including ethical deployment, regulatory issues, and its clinical superiority over traditional statistics and decision making. Several clinical applications of AI in orthopedics include the measurement of bone dimensions, and management of fractures, spinal problems and arthroplasty. It is an innovative way of using the available information to efficiently perform complex cases. This technology supports the orthopedic surgeon in the appropriate selection of surgical implants. It is a promising technology to improve the outcomes of orthopedic surgery.
The present review discusses the role of AI in foot and ankle surgery, focusing on cost implications, potential limitations and future perspectives.
Role of AI in foot and ankle surgery
Diagnostics
With many patients seeing nonorthopedic care providers for foot and ankle radiograph interpretation, DL and AI can be important in getting patients accurately and quickly diagnosed and referred to more specialized providers. Convoluted neural networks (CNNs), a form of DL, recognize visual patterns from raw image pixels which makes them potentially useful for medical imaging. While CNNs developed for radiographic images demonstrate high fracture detection, they are ultimately limited in that radiographs provide only a 2D representation of 3D joints. To address this, AI for ankle and foot fracture detection expands beyond radiographs to computed tomography (CT) imaging as well. Through de novo and pretrained CNNs, DL has been found to successfully detect and accurately classify 92–98% of Sanders calcaneal fracture types [7].
Robotic applications
The field of robotics for use in surgery is not limited to robotic arm applications for intraoperative assistance. AI has been also advocated for imaging analysis, patient-specific instrumentation in preoperative planning, and robotics-aided rehabilitation [8,9,10].
Management of fractures
Several authors have described the use of AI in the diagnosis and treatment of ankle and foot fractures. Ashkani-Esfahani et al. internally validated two deep convolutional neural networks (DCNN) for identifying ankle fractures from radiographs and achieved a near-perfect area under the curve (AUC) of 0.99 [11]. Kitamura et al. internally validated 5 separate CNNs for detecting ankle fractures from plain radiographs and achieved a fair fracture detection accuracy of 81% [12]. Prijs et al. internally and externally validated a DL model for detecting, classifying, and localizing ankle fractures from plain radiographs and achieved an excellent AUC of 0.92 and an accuracy of 99 % on external validation [13]. Guermazi et al. internally validated a DL model for detecting fractures from foot and ankle plain radiographs, which performed excellently with an AUC of 0.97, sensitivity per patient of 93%, and specificity per patient of 93% [14]. Olczak et al. internally validated neural network models for classifying ankle fractures from radiographs according to the AO Foundation/Orthopaedic Trauma Association (AO/OTA) 2018 classification, which showed fair to excellent performance with AUCs ranging from 0.79 to 0.99 in classifying AO types [15]. Pinto Dos Santos et al. internally validated a CNN for detecting fractures in anteroposterior ankle radiographs, which performed well with an AUC of 0.85 [16]. Ashkani-Esfahani et al. internally validated 2 DCNN models for detecting Lisfranc instability from single-view (anteroposterior) and 3‑view radiographs (anteroposterior, lateral, oblique), which performed excellently with AUCs ranging from 0.925 to 0.994 [11]. Aghnia Farda et al. internally validated a CNN model for classifying calcaneal fractures on CT images into the Sanders system, which performed well with a classification accuracy of nearly 72% after augmenting the data 17. Pranata et al. internally validated 2 separate DCNN models for detecting the presence or absence of calcaneal fractures on CT images and achieved an excellent accuracy of 98% [17]. Hendrickx et al. internally validated 4 ML and DL models for predicting patients with tibial shaft fractures and associated occult posterior malleolar fractures. The models performed well with AUCs ranging from 0.81 to 0.89 [18]. Oosterhoff et al. internally validated 5 models for predicting posterior malleolar involvement in distal tibial shaft fractures using the same data set as that in the previously described study by Hendrickx et al [19]. Oosterhoff et al. found that all the models performed well with AUCs 0.80 (highest 0.89) and 4 of 5 having a Brier score of 0.11 [19].
Tendinopathies
Wang et al. internally validated several radiomics-based ML models for diagnosing Achilles tendinopathy from ultrasonographic images in skiers and achieved an excellent AUC of 0.99, 90% sensitivity, and 100% specificity [20]. Kapiński et al. internally validated several DL models to classify Achilles tendons injuries on MRI scans, achieving a maximum accuracy of 97.6%, a sensitivity of 98.3%, and a specificity of 99.45% [21]. Merrill et al. internally validated a logistic regression and gradient boosting model for predicting short-term complications, including mortality and readmissions, in patients who have undergone open reduction and internal fixation (ORIF) in acute ankle fractures. Both models performed similarly, with AUCs for gradient boosting ranging from 0.6979 to 0.7580 and AUCs for logistic regression ranging from 0.7101 to 0.7583 [22].
Hallux valgus
Li et al. aimed to internally validate a DL model to detect 18 anatomical landmarks from weight-bearing radiographs, including the hallux valgus angle (HVA), hallux interphalangeal angle (HIA), first-second intermetatarsal angle (IMA), and distal metatarsal articular angle (DMAA). The observed (manual by a radiologist) and predicted (model) values of the 4 angles correlated well (intraclass correlation: 0.89–0.96, r = 0.81–0.97) [23]. Day et al. aimed to assess the performance of an AI-based software that automatically measures the M1–M2 IMA from weight-bearing cone beam computed tomography (WBCT) scans in patients with hallux valgus. The AI-based software was faster than manual measurements, correlated well with manual measurements, and had higher and nearly perfect test-retest reliability (0.99 intrasoftware intraclass correlation coefficient for both 3D and 2D IMA) [24]. Wang et al. validated a support vector machine model to classify patients with symptomatic hallux valgus using HVA, IMA, and DMAA, with a fair accuracy of 76.4% [20].
Stress fractures
Wang et al. internally and externally tested a DL system for detecting and grading fatigue fractures (a type of stress fracture) from plain radiographs, which performed excellent (AUC 0.911, sensitivity 90.8%) in the detection of fatigue fractures for the foot images and good (AUC 0.877, sensitivity 85.5%) for the tibiofibula images. External validity for grading of fatigue fractures was not demonstrated as the DL system performed poorly with an overall accuracy of 62.9% for the tibiofibular images and an accuracy of 61.1% for the foot images [20].
Sports injury
Diniz et al. internally validated one ML model for predicting whether soccer players would return to similar performances after Achilles tendon rupture, achieving a good AUC of 0.81 and a Brier score loss of 0.12 [25]. Lu et al. internally validated many ML models for predicting the occurrence of a lower extremity muscle strain (quadriceps, calf, hamstring, groin) in elite basketball players [26]. Among them, the XGBoost model achieved the highest AUC of 0.840, representing the best-performing model if the Brier score and calibration were also considered [25]. Jauhiainen et al. internally validated 2 ML models for predicting moderate and severe knee and ankle injuries in young basketball and floorball players (age ≤ 21 years), which performed poorly with an AUC of 0.63 for the random forest model and 0.65 for the logistic regression model [27]. Ruiz-Pérez et al. internally validated many ML models to predict lower extremity non-contact soft tissue injury in professional futsal players, which generally performed fairly, with the best model achieving an AUC of 0.767, a sensitivity of 85.1%, and a specificity of 62.1% [28]. Suda et al. internally validated several support vector machine models for classifying running experience levels based on foot-ankle kinematic and kinetic patterns to potentially assist with running rehabilitation and training. The models performed well with classification accuracies of 88.5% for less experienced runners, 87.2% for moderately experienced runners, and 84.6% for experienced runners [29].
Plantar fasciitis
Yin et al. internally validated a neural network model for predicting patients that would achieve the minimum clinically successful therapy (decrease in the visual analogue score, VAS, by 60% or more from baseline) at 6 months following extracorporeal shock wave therapy (ESWT) in patients with chronic plantar fasciitis. The model performed well, with an overall accuracy of 92.5%, a sensitivity of 95.0%, and a specificity of 90.0% [30]. Keijsers et al. internally validated a neural network model for differentiating patients who have forefoot pain and those that do not use plantar pressure data, which performed satisfactorily with an accuracy of 70.4% [31]. Zhu et al. investigated whether AI-assisted ultrasonography-guided needle knife therapy improves the outcomes of patients treated for chronic plantar fasciitis. Patients who were allocated to the AI-assisted group evidenced statistically significant higher American Orthopaedic Foot & Ankle Society (AOFAS), lower plantar fascia elasticity scores and plantar fascia thickness at 2, 4, and 8 weeks of follow-up [32].
Ankle arthroplasty
Hernigou et al. applied AI and ML to assist in conducting their study for developing a method of defining the ideal and patient-specific motion axes of the tibiotalar joint, intending to improve robotic-assisted total ankle arthroplasty (TAA) [33].
Gait abnormalities
Ardhianto et al. applied DL to help with the automated measurement of the foot progression angle (FPA) from plantar pressure images, helping clinicians in assessing gait abnormalities [34].
Miscellaneous applications
Pakhomov et al. applied ML to automate the identification and classification of foot examination findings from clinical notes as normal, abnormal, or not assessed, and their models performed well with overall accuracies ranging from 81% to 87% [35].
Limitations
Spending billions of dollars on AI technologies, humans are still dealing with the hype of AI and have relatively failed to realize the real uses of this technology and utilize it in the most cost-effective pathway. The value of AI-based solutions should be investigated on several factors, such as ethics, value propositions, the indications of developing the algorithm, safety and risks, potential users, generalizability, quality and validity, and the current limitations to the clinical translation. One of the limitations of using AI was that the images from a single institution will have identical slice thickness and pixel dimension. As other institutions have different imaging technology and image dimensions, the development of deep learning models that have been trained with diverse imaging pools and that can accommodate differences in source imaging is essential.
Future implications
Clinical implications
While foot and ankle surgery has lagged behind other orthopaedic specialities, employing and studying robotics more extensively in this field is necessary. CNNs can be trained for autonomous outcome prediction and are currently focused on fracture detection with projected optimization in a multitude of clinical settings. Lastly, considering post-injury and post-surgey outcomes, robotic foot braces, emulators, and assistive limb devices have a variety of adaptive functions with options for real-time patient feedback that profoundly individualize patient rehabilitation.
Total ankle arthroplasty
Advancements in robotic-assisted TKA and THA demonstrated good clinical outcomes, showing a promising future for application in TAA; however, because of the broad range of foot and ankle surgery with lower volumes in singular procedures than arthroplasty, significant cost barriers exist for the widespread adoption of these technologies. Translational cadaveric studies might help clarify the native mechanical strains and injury biomechanics of ankle joints, test the current TAA systems, and introduce novel machinery for hands-off fracture reduction. At the clinical end, robotics and computer-based systems are being employed for increased precision in TAA and trauma, but these developments are less extensive when contrasted with THA and TKA robotics. Therefore, contained air solutions (CAS) and robots with open technological capacities will likely be more widely adopted in the coming years for use in the foot and ankle; however, improving implant positioning with robotic-assisted TAA can lead to a reduction in long-term healthcare costs, especially given the high failure rates of TAA compared to other joint replacements. If open robotic systems are also developed with capabilities for other procedures that often accompany TAA, such as soft tissue manipulations, longitudinal costs and outcomes will likely be significantly improved both in the operative suite and for patient quality of life.
Prosthetics and orthotics
With future improvements in ankle prostheses, orthotics, and therapeutics on the horizon, further work would help optimize the design of these systems to create more lightweight devices to reduce mechanical work on behalf of the user and to recreate better natural motion [36]. The expansion of the ankle orthosis to a foot-ankle-knee orthosis for more debilitating pathologies has also been described in the literature [37]. Other suggestions include individualized protocols that are tailored to individual patient needs rather than a standardized, one size- fits all protocol. Ultimately, patients will benefit from these technologies through modifiable products promoting individualized recovery, lending to improved post-surgery outcomes.
Healthcare management
Advancements in AI and DL will allow for incorporation in the primary care and acute care settings for increased efficiency and accuracy of ankle pathology diagnoses. Especially regarding scenarios in which practitioners are less familiar with complex orthopedic injuries, these systems can close a gap in knowledge in practice while decreasing the cost of care and time spent interpreting radiographic imaging for more swift referrals, treatment plans, and time to surgical intervention. Given the impressive precision and accuracy of these algorithms, another application is telehealth, allowing for remote diagnostics, potentially without a radiologist’s interpretation. Beyond fracture detection, AI systems can also be employed to inform surgeons of patient-specific projected outcomes based on prior data patterns, answering questions such as “What is my patient’s risk of reoperation or implant failure?” or “How long until this patient is back to work?”.
Research
There exists a vast potential for the application of robotics in the realms of preclinical and translational research, clinical evaluation (e.g., with AI), preoperative planning, and CAS robotics, among others. Future research should be aimed at incorporating robotic technologies specifically into surgical procedures and clinical practice, for which cadaveric translational studies have proven to be an accurate and replicable pipeline.
In vitro and in vivo gait simulators can begin to transition to human subjects; however, less invasive versions should be first developed. Additionally, because most cadaveric models in the past have been static with one plane of motion, employing more dynamic robotic simulators with more degrees of freedom will allow for a more realistic positioning of the specimens to represent biological motion better. Moreover, these static simulators apply only one or two dimensions of action, such as torque or axial load, over fixed ranges of motion. With knowledge of the complexity of joint loading and strain, it would be of interest to apply these concepts to robotic systems to mirror joint kinematics during daily activities such as walking, lunging, and pivoting. This would also necessitate quantification of these types of loads during these activities, which has yet to be elucidated. This research will enrich our understanding of the ankle joint, which can be directly applied to surgical planning and postoperative therapy and return to motion.
Cost implications
Currently, the companies providing AI software are charging hefty fees. This is mainly because of the money which goes into research. This field is at present constantly evolving and once it is streamlined, the cost is bound to come down. Also, integration into the system of healthcare globally will increase the volume of data as well as the users. This in turn will attract multiple companies to offer this technology at a much more competitive rate.
Conclusion
AI is spreading in foot and ankle surgery, but most models lack external validation. Currently, the majority of the models are being used for image interpretation and are performing excellently in doing so, but model performance is not robust for clinical predictions. More subject areas need to be explored in foot and ankle surgery, and models with better performance and external validation are required. The materials and methods should be described with sufficient detail to allow others to replicate and build on the published results. Please note that the publication of your manuscript implies that you must make all materials, data, computer code, and protocols associated with the publication available to readers. Please disclose at the submission stage any restrictions on the availability of materials or information. New methods and protocols should be described in detail while well-established methods can be briefly described and appropriately cited.
Abbreviations
- AI:
-
Artificial intelligence
- AOFAS:
-
American Orthopaedic Foot & Ankle Society
- AO/OTA:
-
AO Foundation/Orthopaedic Trauma Association
- AUC:
-
Area under curve
- CNN:
-
Convoluted neural network
- CT:
-
Computer tomography
- DCNN:
-
Deep convolutional neural network
- DL:
-
Deep learning
- DMAA:
-
Distal metatarsal articular angle
- ESWT:
-
Extracorporeal shock wave therapy
- FPA:
-
Foot progression angle
- HIA:
-
Hallux interphalangeal angle
- HOS-SS:
-
Hip outcome score-sports subscale
- HVA:
-
Hallux valgus angle
- IMA:
-
Intermetatarsal angle
- MCID:
-
Minimal clinically important difference
- ML:
-
Machine learning
- MRI:
-
Magnetic resonance imaging
- TAA:
-
Total ankle arthroplasty
- THA:
-
Total hip arthroplasty
- TKA:
-
Total knee arthroplasty
- VAS:
-
Visual analogue score
- WBCT:
-
Weight-bearing cone beam computed tomography
References
Haleem A, Vaishya R, Javaid M, Khan I (2020) Artificial Intelligence (AI) applications in orthopaedics: An innovative technology to embrace. J Clin Orthop Trauma 11:80–81
Klemt C, Laurencin S, Alpaugh K et al (2022) The utility of machine learning algorithms for the prediction of early revision surgery after primary total hip arthroplasty. J Am Acad Orthop Surg 30(11):513–522
Jo C, Ko S, Shin WC et al (2020) Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm. Knee Surg Sports Traumatol Arthrosc 28(6):17571764. https://doi.org/10.1007/s00167-019-05602-3
Merali Z, Wang JZ, Badhiwala JH, Witiw CD, Wilson JR, Fehlings MG (2021) A deep learning model for detection of cervical spinal cord compression in MRI scans. Sci Rep 11(1):10473. https://doi.org/10.1038/s41598-021-89848
Kunze KN, Polce EM, Clapp I, Nwachukwu BU, Chahla J, Nho SJ (2021) Machine learning algorithms predict functional improvement after hip arthroscopy for femoroacetabular impingement syndrome in athletes. J Bone Joint Surg Am 103(12):1055–1062. https://doi.org/10.2106/JBJS.20.01640
Xu W, Shu L, Gong P et al (2022) A deep-learning aided diagnostic system in assessing developmental dysplasia of the hip on pediatric pelvic radiographs. Front Pediatr. https://doi.org/10.3389/fped.2021.785480
Pranata YD, Wang KC, Wang JC, Idram I, Lai JY, Liu JW, Hsieh IH (2019) Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput Methods Programs Biomed 171:27–37. https://doi.org/10.1016/j.cmpb.2019.02.006
Kfuri M, Crist BD, Stannard JP (2022) Preoperative Planning and Preservation of the Knee with Complex Osteotomies. Mo Med 119(2):144–151
Tiefenboeck S, Sesselmann S, Taylor D, Forst R, Seehaus F (2022) Preoperative planning of total knee arthroplasty: reliability of axial alignment using a three-dimensional planning approach. Acta Radiol 63(8):1051–1061. https://doi.org/10.1177/02841851211029076
Lambrechts A, Wirix-Speetjens R, Maes F, Van Huffel S (2022) Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty. Front Robot AI 9:899349. https://doi.org/10.3389/frobt.2022.840282
Ashkani-Esfahani S, Mojahed Yazdi R, Bhimani R et al (2022) Detection of ankle fractures using deep learning algorithms. Foot Ankle Surg 28(8):1259–1265. https://doi.org/10.1016/j.fas.2022.05.005
Kitamura G, Chung CY, Moore BE 2nd (2019) Ankle fracture detection utilizing a convolutional neural network ensemble implemented with a small sample, de novo training, and multiview incorporation. J Digit Imaging 32(4):672–677. https://doi.org/10.1007/s10278-018-0167
Prijs J, Liao Z, To MS et al (2022) Development and external validation of automated detection, classification, and localization of ankle fractures: inside the black box of a convolutional neural network (CNN). Eur J Trauma Emerg Surg. https://doi.org/10.1007/s00068-022-02136-1
Guermazi A, Tannoury C, Kompel AJ et al (2022) Improving radiographic fracture recognition performance and efficiency using artificial intelligence. Radiology 302(3):627–636. https://doi.org/10.1148/radiol.210937
Olczak J, Emilson F, Razavian A, Antonsson T, Stark A, Gordon M (2021) Ankle fracture classification using deep learning: automating detailed AO Foundation/Orthopedic Trauma Association (AO/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification. Acta Orthop 92(1):102–108. https://doi.org/10.1080/17453674.2020.1837420
Dos PSD, Brodehl S, Baeßler B et al (2019) Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs. Insights Imaging 10(1):93. https://doi.org/10.1186/s13244-019-0777
Aghnia Farda N, Lai JY, Wang JC, Lee PY, Liu JW, Hsieh IH (2021) Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques. Injury 52(3):616–624. https://doi.org/10.1016/j.injury.2020.09.010
Hendrickx LAM, Sobol GL, Langerhuizen DWG et al (2020) A machine learning algorithm to predict the probability of (occult) posterior malleolar fractures associated with tibial shaft fractures to guide “malleolus first” fixation. J Orthop Trauma 34(3):131–138. https://doi.org/10.1097/BOT.0000000000001663
Oosterhoff JHF, Gravesteijn BY, Karhade AV et al (2022) Feasibility of machine learning and logistic regression algorithms to predict outcome in orthopaedic trauma surgery. J Bone Joint Surg Am 104(6):544–551. https://doi.org/10.2106/JBJS.21.00341
Wang L, Wen D, Yin Y et al (2022) Musculoskeletal ultrasound image-based radiomics for the diagnosis of achilles tendinopathy in skiers. J Ultrasound Med. https://doi.org/10.1002/jum.16059
Kapiński N, Zieliński J, Borucki BA et al (2019) Monitoring of the Achilles tendon healing process: can artificial intelligence be helpful? Acta Bioeng Biomech 21(1):103–111
Merrill RK, Ferrandino RM, Hoffman R, Shaffer GW, Ndu A (2019) Machine learning accurately predicts short-term outcomes following open reduction and internal fixation of ankle fractures. J Foot Ankle Surg 58(3):410–416. https://doi.org/10.1053/j.jfas.2018.09.004
Li T, Wang Y, Qu Y, Dong R, Kang M, Zhao J (2022) Feasibility study of hallux valgus measurement with a deep convolutional neural network based on landmark detection. Skelet Radiol 51(6):1235–1247. https://doi.org/10.1007/s00256-021-03939-w
Day J, de Cesar Netto C, Richter M et al (2021) Evaluation of a weightbearing CT artificial intelligence-based automatic measurement for the M1–M2 intermetatarsal angle in hallux valgus. Foot Ankle Int 42(11):1502–1509. https://doi.org/10.1177/10711007211015177
Diniz P, Abreu M, Lacerda D et al (2022) Pre-injury performance is most important for predicting the level of match participation after Achilles tendon ruptures in elite soccer players: a study using a machine learning classifier. Knee Surg Sports Traumatol Arthrosc 30(12):4225–4237. https://doi.org/10.1007/s00167-022-07082-4
Lu Y, Pareek A, Lavoie-Gagne OZ, Forlenza EM, Patel BH, Reinholz AK, Forsythe B, Camp CL (2022) Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes. Orthop J Sports Med 10(7):23259671221111742. https://doi.org/10.1177/23259671221111742
Jauhiainen S, Kauppi JP, Leppänen M et al (2021) New machine learning approach for detection of injury risk factors in young team sport athletes. Int J Sports Med 42(2):175–182. https://doi.org/10.1055/a-1231-5304
Ruiz-Pérez I, López-Valenciano A, Hernández-Sánchez S et al (2021) A field-based approach to determine soft tissue injury risk in elite futsal using novel machine learning techniques. Front Psychol 12:610210. https://doi.org/10.3389/fpsyg.2021.610210
Suda EY, Watari R, Matias AB, Sacco ICN (2020) Recognition of foot-ankle movement patterns in long-distance runners with different experience levels using support vector machines. Front Bioeng Biotechnol 8:576. https://doi.org/10.3389/fbioe.2020.00576
Yin M, Ma J, Xu J et al (2019) Use of artificial neural networks to identify the predictive factors of extracorporeal shock wave therapy treating patients with chronic plantar fasciitis. Sci Rep 9(1):4207. https://doi.org/10.1038/s41598-01939026-3
Keijsers NLW, Stolwijk NM, Louwerens JWK, Duysens J (2013) Classification of forefoot pain based on plantar pressure measurements. Clin Biomech 28(3):350356. https://doi.org/10.1016/j.clinbiomech.2013.01.012
Zhu S, Niu Y, Wang J, Xu D, Li Y (2022) Artificial intelligence technology combined with ultrasound-guided needle knife interventional treatment of PF: improvement of pain, fascia thickness, and ankle-foot function in patients. Comput Math Methods Med 2022:3021320. https://doi.org/10.1155/2022/3021320
Hernigou P, Olejnik R, Safar A, Martinov S, Hernigou J, Ferre B (2021) Digital twins, artificial intelligence, and machine learning technology to identify a real personalized motion axis of the tibiotalar joint for robotics in total ankle arthroplasty. Int Orthop 45(9):2209–2217. https://doi.org/10.1007/s00264-02105175-2
Ardhianto P, Subiakto RBR, Lin CY et al (2022) A deep learning method for foot progression angle detection in plantar pressure images. Sensors 22(7):2786. https://doi.org/10.3390/s22072786
Pakhomov SVS, Hanson PL, Bjornsen SS, Smith SA (2008) Automatic classification of foot examination findings using clinical notes and machine learning. J Am Med Inform Assoc 15(2):198–202. https://doi.org/10.1197/jamia.M2585
Hussain S, Jamwal PK, Ghayesh MH (2017) State-of-the-art robotic devices for ankle rehabilitation: Mechanism and control review. Proc Inst Mech Eng H 231:1224–1234
Alvarez-Perez MG, Garcia-Murillo MA, Cervantes-Sánchez JJ (2020) Robot-assisted ankle rehabilitation: A review. Disabil Rehabil Assist Technol 15:394–408
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Funding
Open Access funding enabled and organized by Projekt DEAL.
Author information
Authors and Affiliations
Contributions
RV: conceptualization, literature search, manuscript writing, editing, and final approval, AV: literature search, manuscript writing, editing, and final approval, FM: literature search, manuscript editing, and final approval.
Corresponding author
Ethics declarations
Conflict of interest
A. Vaish, F. Migliorini and R. Vaishya declare that they have no competing interests.
For this article no studies with human participants or animals were performed by any of the authors. All studies mentioned were in accordance with the ethical standards indicated in each case.
Additional information
Registration and protocol
The present review was not registered.
Availability of data and material
No dataset has been generated during the current study.
Scan QR code & read article online
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Vaish, A., Migliorini, F. & Vaishya, R. Artificial intelligence in foot and ankle surgery: current concepts. Orthopädie 52, 1011–1016 (2023). https://doi.org/10.1007/s00132-023-04426-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00132-023-04426-x