Artificial intelligence as an adjunctive tool in hand and wrist surgery: a review

Abstract

Artificial intelligence (AI) is currently utilized across numerous medical disciplines. Nevertheless, despite its promising advancements, AI’s integration in hand surgery remains in its early stages and has not yet been widely implemented, necessitating continued research to validate its efficacy and ensure its safety. Therefore, this review aims to provide an overview of the utilization of AI in hand surgery, emphasizing its current application in clinical practice, along with its potential benefits and associated challenges. A comprehensive literature search was conducted across PubMed, Embase, Medline, and Cochrane libraries, adhering to the Preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The search focused on identifying articles related to the application of AI in hand surgery, utilizing multiple relevant keywords. Each identified article was assessed based on its title, abstract, and full text. The primary search identified 1,228 articles; after the application of inclusion/exclusion criteria and manual bibliography search of included articles, a total of 98 articles were covered in this review. AI’s primary application in hand and wrist surgery is diagnostic, which includes hand and wrist fracture detection, carpal tunnel syndrome (CTS), avascular necrosis (AVN), and osteoporosis screening. Other applications include residents’ training, patient-doctor communication, surgical assistance, and outcome prediction. Consequently, AI is a very promising tool that has numerous applications in hand and wrist surgery, though further research is necessary to fully integrate it into clinical practice.

Keywords

Artificial intelligence / hand surgery / wrist surgery

References

[1] Mintz Y.Introduction to artificial intelligence in medicine.Minim Invasive Ther Allied Technol2019;28:73-81

[2] Jarvis T,Rebecca AM.Artificial intelligence in plastic surgery: current applications, future directions, and ethical implications.Plast Reconstr Surg Glob Open2020;8:e3200 PMCID:PMC7647513

[3] Mantelakis A,Sorooshian P.Machine learning demonstrates high accuracy for disease diagnosis and prognosis in plastic surgery.Plast Reconstr Surg Glob Open2021;9:e3638 PMCID:PMC8225366

[4] Lopez C,Salameh T.An unsupervised machine learning method for discovering patient clusters based on genetic signatures.J Biomed Inform2018;85:30-9 PMCID:PMC6621561

[5] Jiang F,Zhi H.Artificial intelligence in healthcare: past, present and future.Stroke Vasc Neurol2017;2:230-43 PMCID:PMC5829945

[6] Ahmadi N,Ghasemi A,Motamedian SR.Applications of machine learning in facial cosmetic surgeries: a scoping review.Aesthetic Plast Surg2023;47:1377-93

[7] Lakhani P,Hutson RK.Machine learning in radiology: applications beyond image interpretation.J Am Coll Radiol2018;15:350-9

[8] Pesapane F,Sardanelli F.Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine.Eur Radiol Exp2018;2:35 PMCID:PMC6199205

[9] Zhou XY,Shen M.Application of artificial intelligence in surgery.Front Med2020;14:417-30

[10] Loftus TJ,Filiberto AC.Artificial intelligence and surgical decision-making.JAMA Surg2020;155:148-58 PMCID:PMC7286802

[11] Spoer DL,Dekker PK.A systematic review of artificial intelligence applications in plastic surgery: looking to the future.Plast Reconstr Surg Glob Open2022;10:e4608 PMCID:PMC9722565

[12] Dorfman R,Saadat S.Making the subjective objective: machine learning and rhinoplasty.Aesthet Surg J2020;40:493-8

[13] Eldaly AS,Torres-Guzman RA.Simulation and artificial intelligence in rhinoplasty: a systematic review.Aesthetic Plast Surg2022;46:2368-77

[14] Kanevsky J,Gaster R,Lin S.Big data and machine learning in plastic surgery: a new frontier in surgical innovation.Plast Reconstr Surg2016;137:890e-7e

[15] Tricco AC,Zarin W.PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation.Ann Intern Med2018;169:467-73

[16] Thibaut G,Liverneaux P.Does Google’s Bard Chatbot perform better than ChatGPT on the European hand surgery exam?.Int Orthop2024;48:151-8

[17] Arango SD,Zeitlin J.The performance of ChatGPT on the American society for surgery of the hand self-assessment examination.Cureus2024;16:e58950 PMCID:PMC11126365

[18] Ghanem D,El Bachour J.ChatGPT earns American Board Certification in Hand Surgery.Hand Surg Rehabil2024;43:101688

[19] Crook BS,Hurley ET,Pidgeon TS.Evaluation of online artificial intelligence-generated information on common hand procedures.J Hand Surg Am2023;48:1122-7

[20] Seth I,Bulloch G,Bo Thomsen J.Artificial or augmented authorship? A conversation with a chatbot on base of thumb arthritis.Plast Reconstr Surg Glob Open2023;11:e4999 PMCID:PMC10219695

[21] Leypold T,Boos A.Can AI think like a plastic surgeon? Evaluating GPT-4’s clinical judgment in reconstructive procedures of the upper extremity.Plast Reconstr Surg Glob Open2023;11:e5471 PMCID:PMC10718352

[22] Seth I,Rodwell A.Exploring the role of a large language model on carpal tunnel syndrome management: an observation study of ChatGPT.J Hand Surg Am2023;48:1025-33

[23] Seth I,Xie Y,Rozen WM.Exploring the role of artificial intelligence chatbot on the management of scaphoid fractures.J Hand Surg Eur Vol2023;48:814-8

[24] Ajmera P,Ariyaratne S.Validity of ChatGPT-generated musculoskeletal images.Skeletal Radiol2024;53:1583-93

[25] Olczak J,Maki A.Artificial intelligence for analyzing orthopedic trauma radiographs.Acta Orthop2017;88:581-6 PMCID:PMC5694800

[26] Lee KC,Kang CH.Clinical validation of an artificial intelligence model for detecting distal radius, ulnar styloid, and scaphoid fractures on conventional wrist radiographs.Diagnostics2023;13:1657 PMCID:PMC10178713

[27] Lindsey R,Chopra S.Deep neural network improves fracture detection by clinicians.Proc Natl Acad Sci U S A2018;115:11591-6 PMCID:PMC6233134

[28] Cohen M,Sanchez J.Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs.Eur Radiol2023;33:3974-83

[29] Hardalaç F,Peker O.Fracture detection in wrist x-ray images using deep learning-based object detection models.Sensors2022;22:1285 PMCID:PMC8838335

[30] Lysdahlgaard S.Utilizing heat maps as explainable artificial intelligence for detecting abnormalities on wrist and elbow radiographs.Radiography2023;29:1132-8

[31] Alammar Z,Zhang J,Lafta W.Deep transfer learning with enhanced feature fusion for detection of abnormalities in X-ray images.Cancers2023;15:4007 PMCID:PMC10417687

[32] Jacques T,Ventre J,Cotten A.Commercially-available AI algorithm improves radiologists’ sensitivity for wrist and hand fracture detection on X-ray, compared to a CT-based ground truth.Eur Radiol2024;34:2885-94

[33] Kim DH.Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks.Clin Radiol2018;73:439-45

[34] Gan K,Lin Y.Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments.Acta Orthop2019;90:394-400 PMCID:PMC6718190

[35] Thian YL,Jagmohan P,Chan VEY.Convolutional neural networks for automated fracture detection and localization on wrist radiographs.Radiol Artif Intell2019;1:e180001 PMCID:PMC8017412

[36] Oka K,Yoshii Y,Iwahashi T.Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays.J Orthop Surg Res2021;16:694 PMCID:PMC8620959

[37] Russe MF,Tran PH.AI-based X-ray fracture analysis of the distal radius: accuracy between representative classification, detection and segmentation deep learning models for clinical practice.BMJ Open2024;14:e076954 PMCID:PMC10823998

[38] Zhang J,Lin H.Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures.Front Med2023;10:1224489 PMCID:PMC10471443

[39] Anttila TT,Mäkelä TO.Detecting distal radius fractures using a segmentation-based deep learning model.J Digit Imaging2023;36:679-87 PMCID:PMC10039188

[40] Mert S,Brauer J.Diagnostic power of ChatGPT 4 in distal radius fracture detection through wrist radiographs.Arch Orthop Trauma Surg2024;144:2461-7 PMCID:PMC11093861

[41] Ozkaya E,Bulut T,Ozuysal M.Evaluation of an artificial intelligence system for diagnosing scaphoid fracture on direct radiography.Eur J Trauma Emerg Surg2022;48:585-92

[42] Hendrix N,Vernhout B.Development and validation of a convolutional neural network for automated detection of scaphoid fractures on conventional radiographs.Radiol Artif Intell2021;3:e200260 PMCID:PMC8329964

[43] Hendrix N,van Dijke K.Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist.Eur Radiol2023;33:1575-88 PMCID:PMC9935716

[44] Tung Y,Liao Y.High-performance scaphoid fracture recognition via effectiveness assessment of artificial neural networks.Appl Sci2021;11:8485

[45] Yang TH,Li RS.Scaphoid fracture detection by using convolutional neural network.Diagnostics2022;12:895 PMCID:PMC9024757

[46] Bulstra AEJ; Machine Learning Consortium. A machine learning algorithm to estimate the probability of a true scaphoid fracture after wrist trauma. J Hand Surg Am 2022;47:709-18.

[47] Langerhuizen DWG,Janssen SJ.Is deep learning on par with human observers for detection of radiographically visible and occult fractures of the scaphoid?.Clin Orthop Relat Res2020;478:2653-9 PMCID:PMC7571968

[48] Yoon AP,Kane RL,Lin C.Development and validation of a deep learning model using convolutional neural networks to identify scaphoid fractures in radiographs.JAMA Netw Open2021;4:e216096 PMCID:PMC8103226

[49] Raisuddin AM,Nevalainen M.Critical evaluation of deep neural networks for wrist fracture detection.Sci Rep2021;11:6006 PMCID:PMC7971048

[50] Janisch M,Hržić F.Pediatric radius torus fractures in x-rays - how computer vision could render lateral projections obsolete.Front Pediatr2022;10:1005099 PMCID:PMC9794847

[51] Zech JR, Ezuma CO, Patel S, et al. Artificial intelligence improves resident detection of pediatric and young adult upper extremity fractures. Skeletal Radiol 2024.

[52] Smith AM,Annapureddy SR,Amis AA.The porcine forelimb as a model for human flexor tendon surgery.J Hand Surg Br2005;30:307-9

[53] Ilie VG,Dobreanu C,Luchian S.Training of microsurgical skills on nonliving models.Microsurgery2008;28:571-7

[54] Watanabe T,Yamada E,Fujita K.The accuracy of a screening system for carpal tunnel syndrome using hand drawing.J Clin Med2021;10:4437 PMCID:PMC8509818

[55] Orji C,Saavedra Palacios MS.Application of artificial intelligence and machine learning in diagnosing scaphoid fractures: a systematic review.Cureus2023;15:e47732 PMCID:PMC10676208

[56] Zhang J,Melendez S,Jaremko J.Diagnostic accuracy of 3D ultrasound and artificial intelligence for detection of pediatric wrist injuries.Children2021;8:431 PMCID:PMC8224020

[57] Knight J,Keen C.2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence.Sci Rep2023;13:14535 PMCID:PMC10477281

[58] Caratsch L,Caorsi M.Detection and grading of radiographic hand osteoarthritis using an automated machine learning platform.ACR Open Rheumatol2024;6:388-95 PMCID:PMC11168904

[59] Overgaard BS,Terslev L,Just SA.Artificial intelligence model for segmentation and severity scoring of osteophytes in hand osteoarthritis on ultrasound images.Front Med2024;11:1297088 PMCID:PMC10944993

[60] Loos NL, Hoogendam L, Souer JS, et al; the Hand-Wrist Study Group. Machine learning can be used to predict function but not pain after surgery for thumb carpometacarpal osteoarthritis. Clin Orthop Relat Res 2022;480:1271-84. PMCID:PMC9191288

[61] Koyama T,Toriumi M.A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study.JMIR Mhealth Uhealth2021;9:e26320 PMCID:PMC8005991

[62] Faeghi F,Acharya UR.Accurate automated diagnosis of carpal tunnel syndrome using radiomics features with ultrasound images: a comparison with radiologists’ assessment.Eur J Radiol2021;136:109518

[63] Shinohara I,Mifune Y.Using deep learning for ultrasound images to diagnose carpal tunnel syndrome with high accuracy.Ultrasound Med Biol2022;48:2052-9

[64] Mohammadi A,Mirza-Aghazadeh-Attari M,Acharya UR.Deep radiomics features of median nerves for automated diagnosis of carpal tunnel syndrome with ultrasound images: a multi-center study.J Ultrasound Med2023;42:2257-68

[65] Kim SW,Shin D.Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome.BMC Musculoskelet Disord2023;24:524 PMCID:PMC10294449

[66] Kuroiwa T,Starlinger J.Deep learning estimation of median nerve volume using ultrasound imaging in a human cadaver model.Ultrasound Med Biol2022;48:2237-48

[67] Tsamis KI,Gourgiotis I,Sarmas I.Automatic electrodiagnosis of carpal tunnel syndrome using machine learning.Bioengineering2021;8:181 PMCID:PMC8615235

[68] Bakalis D,Ntais E,Tsamis KI.Carpal tunnel syndrome automated diagnosis: a motor vs. sensory nerve conduction-based approach.Bioengineering2024;11:175 PMCID:PMC10886232

[69] Elseddik M,Elashry A.Predicting CTS diagnosis and prognosis based on machine learning techniques.Diagnostics2023;13:492 PMCID:PMC9914125

[70] Park D,Lee SE.Machine learning-based approach for disease severity classification of carpal tunnel syndrome.Sci Rep2021;11:17464 PMCID:PMC8408248

[71] Harrison CJ,Sidey-Gibbons CJ,McEachan JE.Developing machine learning algorithms to support patient-centered, value-based carpal tunnel decompression surgery.Plast Reconstr Surg Glob Open2022;10:e4279 PMCID:PMC9015194

[72] Hoogendam L, Bakx JAC, Souer JS, Slijper HP, Andrinopoulou ER, Selles RW; Hand Wrist Study Group. Predicting clinically relevant patient-reported symptom improvement after carpal tunnel release: a machine learning approach. Neurosurgery 2022;90:106-13.

[73] Loos NL, Hoogendam L, Souer JS, et al; Hand-Wrist Study Group. Algorithm versus expert: machine learning versus surgeon-predicted symptom improvement after carpal tunnel release. Neurosurgery 2024;95:110-7. PMCID:PMC11155572

[74] Orgiu A,Cannell S,Bennani Y.Enhancing wrist arthroscopy: artificial intelligence applications for bone structure recognition using machine learning.Hand Surg Rehabil2024;101717

[75] Henn D,Barrera JA.Robotics in plastic surgery: it’s here.Plast Reconstr Surg2023;152:239-49

[76] Mohapatra DP,Tripathy S.Leveraging large language models (LLM) for the plastic surgery resident training: do they have a role?.Indian J Plast Surg2023;56:413-20 PMCID:PMC10663077

[77] Jagiella-Lodise O,Zelenski NA.Can patients rely on ChatGPT to answer hand pathology-related medical questions?.Hand2024;15589447241247246

[78] Amen TB,Subramanian T,Liimakka A.Quality of ChatGPT responses to frequently asked questions in carpal tunnel release surgery.Plast Reconstr Surg Glob Open2024;12:e5822 PMCID:PMC11098167

[79] Croen BJ,Berns E.Evaluation of patient education materials from large-language artificial intelligence models on carpal tunnel release.Hand2024;15589447241247332

[80] Pohl NB,Rivlin M.A quality and readability comparison of artificial intelligence and popular health website education materials for common hand surgery procedures.Hand Surg Rehabil2024;43:101723

[81] Browne R,Hurley CM,O’Sullivan JB.ChatGPT-4 can help hand surgeons communicate better with patients.J Hand Surg Glob Online2024;6:436-8 PMCID:PMC11133925

[82] Wernér K,Hulkkonen S,Haapamäki V.Detecting avascular necrosis of the lunate from radiographs using a deep-learning model.J Imaging Inform Med2024;37:706-14 PMCID:PMC11031541

[83] Lin KY,Han JY.Deep learning to detect triangular fibrocartilage complex injury in wrist MRI: retrospective study with internal and external validation.J Pers Med2022;12:1029 PMCID:PMC9322609

[84] Anttila TT,Pierides G,Laitinen MK.Enchondroma detection from hand radiographs with an interactive deep learning segmentation tool - a feasibility study.J Clin Med2023;12:7129 PMCID:PMC10672653

[85] Kim KB,Park HJ.Intelligent automatic segmentation of wrist ganglion cysts using DBSCAN and fuzzy C-means.Diagnostics2021;11:2329 PMCID:PMC8700243

[86] Buul MM, Bos KE, Dijkstra PF, van Beek EJ, Broekhuizen AH. Carpal instability, the missed diagnosis in patients with clinically suspected scaphoid fracture.Injury1993;24:257-62

[87] N,Maresch B.Hendrix N, Hendrex W, Maresch B, et al. Artificial intelligence for automated detection and measurements of carpal instability signs on conventional radiographs. Eur Radiol 2024.

[88] Gu F,Cai C.Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation.Front Neurol2022;13:1052505 PMCID:PMC9767954

[89] Baxter NB,Byrd JN,Singh K.Predicting persistent opioid use after hand surgery: a machine learning approach.Plast Reconstr Surg2024;154:573-80

[90] Miller R,Horwitz MD.Insights and trends review: artificial intelligence in hand surgery.J Hand Surg Eur Vol2023;48:396-403

[91] Keller M,Thieringer F.Artificial intelligence in patient-specific hand surgery: a scoping review of literature.Int J Comput Assist Radiol Surg2023;18:1393-403 PMCID:PMC10363089

[92] Kraus M,Konen E,Klang E.Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis.Eur Radiol2024;34:4341-51 PMCID:PMC11213739

[93] Oeding JF,Messer CJ.Diagnostic performance of artificial intelligence for detection of scaphoid and distal radius fractures: a systematic review.J Hand Surg Am2024;49:411-22

[94] Singh G,Cho W,Son KC.Hybrid deep learning approach for automatic detection in musculoskeletal radiographs.Biology2022;11:665 PMCID:PMC9138246