Treffer: [Artificial intelligence and novel approaches for treatment of non-union in bone : From established standard methods in medicine up to novel fields of research].
Title:
[Artificial intelligence and novel approaches for treatment of non-union in bone : From established standard methods in medicine up to novel fields of research].
Transliterated Title:
Künstliche Intelligenz und Ausblick auf Anwendungsfelder in der Pseudarthrosentherapie : Von etablierten Standardmethoden in der Medizin hin zu neuen Forschungsfeldern.
Authors:
Reumann MK; Klinik für Unfall- und Wiederherstellungschirurgie an der Eberhard Karls Universität Tübingen, BG Klinik Tübingen, Schnarrenbergstr. 95, 72076, Tübingen, Deutschland. mreumann@bgu-tuebingen.de.; Siegfried Weller Institut für Unfallmedizinische Forschung an der Eberhard Karls Universität Tübingen, BG Klinik Tübingen, Tübingen, Deutschland. mreumann@bgu-tuebingen.de., Braun BJ; Klinik für Unfall- und Wiederherstellungschirurgie an der Eberhard Karls Universität Tübingen, BG Klinik Tübingen, Schnarrenbergstr. 95, 72076, Tübingen, Deutschland., Menger MM; Klinik für Unfall- und Wiederherstellungschirurgie an der Eberhard Karls Universität Tübingen, BG Klinik Tübingen, Schnarrenbergstr. 95, 72076, Tübingen, Deutschland., Springer F; Klinik für Diagnostische und Interventionelle Radiologie, Eberhard Karls Universität Tübingen, Tübingen, Deutschland., Jazewitsch J; Siegfried Weller Institut für Unfallmedizinische Forschung an der Eberhard Karls Universität Tübingen, BG Klinik Tübingen, Tübingen, Deutschland., Schwarz T; Siegfried Weller Institut für Unfallmedizinische Forschung an der Eberhard Karls Universität Tübingen, BG Klinik Tübingen, Tübingen, Deutschland., Nüssler A; Siegfried Weller Institut für Unfallmedizinische Forschung an der Eberhard Karls Universität Tübingen, BG Klinik Tübingen, Tübingen, Deutschland., Histing T; Klinik für Unfall- und Wiederherstellungschirurgie an der Eberhard Karls Universität Tübingen, BG Klinik Tübingen, Schnarrenbergstr. 95, 72076, Tübingen, Deutschland., Rollmann MFR; Klinik für Unfall- und Wiederherstellungschirurgie an der Eberhard Karls Universität Tübingen, BG Klinik Tübingen, Schnarrenbergstr. 95, 72076, Tübingen, Deutschland.
Source:
Unfallchirurgie (Heidelberg, Germany) [Unfallchirurgie (Heidelb)] 2022 Aug; Vol. 125 (8), pp. 611-618. Date of Electronic Publication: 2022 Jul 09.
Publication Type:
Journal Article; Review
Language:
German
Journal Info:
Publisher: Springer Medizin Country of Publication: Germany NLM ID: 9918384886306676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2731-703X (Electronic) Linking ISSN: 27317021 NLM ISO Abbreviation: Unfallchirurgie (Heidelb) Subsets: MEDLINE
Imprint Name(s):
Original Publication: [Heidelberg, Germany] : Springer Medizin, [2022]-
MeSH Terms:
References:
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McCarthy J, Minsky ML, Rochester N et al (1956) A proposal for the Dartmouth summer research project on artificial intelligence. Dartmouth Conference. Dartmouth College, Hanover, New Hampshire.
Shortliffe E (1976) Computer-based medical consultations: MYCIN. Elsevier https://doi.org/10.1016/B978-0-444-00179-5.X5001-X. (PMID: 10.1016/B978-0-444-00179-5.X5001-X)
Lalehzarian SP, Gowd AK, Liu JN (2021) Machine learning in orthopaedic surgery. World J Orthop 12:685–699. https://doi.org/10.5312/wjo.v12.i9.685. (PMID: 10.5312/wjo.v12.i9.685346314528472446)
Olczak J, Pavlopoulos J, Prijs J et al (2021) Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal. Acta Orthop 92:513–525. https://doi.org/10.1080/17453674.2021.1918389. (PMID: 10.1080/17453674.2021.1918389339880818519529)
Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349:255–260. https://doi.org/10.1126/science.aaa8415. (PMID: 10.1126/science.aaa841526185243)
Campbell M, Hoane AJ Jr, Hsu F (2002) Deep blue. Artif Intell 134:57–83. https://doi.org/10.1016/S0004-3702(01)00129-1. (PMID: 10.1016/S0004-3702(01)00129-1)
Ferrucci D, Brown E, Chu-Carroll J et al (2010) Building watson: an overview of the deepQA project. Artif Intell Mag 31(3):59–79. https://doi.org/10.1609/aimag.v31i3.2303. (PMID: 10.1609/aimag.v31i3.2303)
Silver D, Huang A, Maddison CJ et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529:484–489. https://doi.org/10.1038/nature16961. (PMID: 10.1038/nature1696126819042)
Wyatt JM, Booth GJ, Goldman AH (2021) Natural language processing and its use in orthopaedic research. Curr Rev Musculoskelet Med 14:392–396. https://doi.org/10.1007/s12178-021-09734-3. (PMID: 10.1007/s12178-021-09734-3347552768577962)
Thirukumaran CP, Zaman A, Rubery PT et al (2019) Natural language processing for the identification of surgical site infections in orthopaedics. J Bone Joint Surg Am 101:2167–2174. https://doi.org/10.2106/JBJS.19.00661. (PMID: 10.2106/JBJS.19.0066131596819)
Phung VH, Rhee EJ (2019) A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Appl Sci. https://doi.org/10.3390/app9214500. (PMID: 10.3390/app9214500)
Myers TG, Ramkumar PN, Ricciardi BF et al (2020) Artificial intelligence and orthopaedics: an introduction for clinicians. J Bone Joint Surg Am 102:830–840. https://doi.org/10.2106/JBJS.19.01128. (PMID: 10.2106/JBJS.19.0112832379124)
Geron A (2019) Hands-on machine learning with Scikit—learn, Keras and tensor flow. O’Reilly Media.
Martins LF (2014) IPython notebook essentials. Packt.
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003. (PMID: 10.1016/j.neunet.2014.09.00325462637)
Zhao W, Davis CE (2011) A modified artificial immune system based pattern recognition approach—an application to clinical diagnostics. Artif Intell Med 52:1–9. https://doi.org/10.1016/j.artmed.2011.03.001. (PMID: 10.1016/j.artmed.2011.03.001215150333108456)
Kohonen T (2006) Self-organizing neural projections. Neural Netw 19:723–733. https://doi.org/10.1016/j.neunet.2006.05.001. (PMID: 10.1016/j.neunet.2006.05.00116774731)
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539. (PMID: 10.1038/nature1453926017442)
Borjali A, Chen AF, Muratoglu OK et al (2020) Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network. J Orthop Res 38:1465–1471. https://doi.org/10.1002/jor.24617. (PMID: 10.1002/jor.2461731997411)
Erne F, Dehncke D, Herath SC et al (2021) Deep learning in the detection of rare fractures—development of a “deep learning convolutional network” model for detecting Acetabular fractures. Z Orthop Unfall. https://doi.org/10.1055/a-1511-8595. (PMID: 10.1055/a-1511-859534359091)
Olczak J, Emilson F, Razavian A et al (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 92:102–108. https://doi.org/10.1080/17453674.2020.1837420. (PMID: 10.1080/17453674.2020.183742033103536)
Kruse C, Eiken P, Vestergaard P (2017) Machine learning principles can improve hip fracture prediction. Calcif Tissue Int 100:348–360. https://doi.org/10.1007/s00223-017-0238-7. (PMID: 10.1007/s00223-017-0238-728197643)
Xue Y, Zhang R, Deng Y et al (2017) A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PLoS ONE 12:e178992. https://doi.org/10.1371/journal.pone.0178992. (PMID: 10.1371/journal.pone.0178992285750705456368)
Begg R, Kamruzzaman J (2005) A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech 38:401–408. https://doi.org/10.1016/j.jbiomech.2004.05.002. (PMID: 10.1016/j.jbiomech.2004.05.00215652537)
Joyseeree R, Abou Sabha R, Mueller H (2015) Applying machine learning to gait analysis data for disease identification. Stud Health Technol Inform 210:850–854. (PMID: 25991275)
Sikka RS, Baer M, Raja A et al (2019) Analytics in sports medicine: implications and responsibilities that accompany the era of big data. J Bone Joint Surg Am 101:276–283. https://doi.org/10.2106/JBJS.17.01601. (PMID: 10.2106/JBJS.17.0160130730488)
Ekegren CL, Edwards ER, de Steiger R et al (2018) Incidence, costs and predictors of non-union, delayed union and mal-union following long bone fracture. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph15122845. (PMID: 10.3390/ijerph15122845305516326313538)
McCoy TH Jr., Fragomen AT, Hart KL et al (2019) Genomewide association study of fracture nonunion using electronic health records. JBMR Plus 3:23–28. https://doi.org/10.1002/jbm4.10063. (PMID: 10.1002/jbm4.1006330680360)
Mills LA, Aitken SA, Simpson A (2017) The risk of non-union per fracture: current myths and revised figures from a population of over 4 million adults. Acta Orthop 88:434–439. https://doi.org/10.1080/17453674.2017.1321351. (PMID: 10.1080/17453674.2017.1321351285086825499337)
Calori GM, Colombo M, Mazza EL et al (2014) Validation of the non-union scoring system in 300 long bone non-unions. Injury 45(Suppl 6):S93–S97. https://doi.org/10.1016/j.injury.2014.10.030. (PMID: 10.1016/j.injury.2014.10.03025457326)
Calori GM, Phillips M, Jeetle S et al (2008) Classification of non-union: need for a new scoring system? Injury 39(Suppl 2):S59–S63. https://doi.org/10.1016/S0020-1383(08)70016-0. (PMID: 10.1016/S0020-1383(08)70016-018804575)
Santolini E, West RM, Giannoudis PV (2020) Leeds-Genoa Non-Union Index: a clinical tool for asessing the need for early intervention after long bone fracture fixation. Int Orthop 44:161–172. https://doi.org/10.1007/s00264-019-04376-0. (PMID: 10.1007/s00264-019-04376-031440889)
Whelan DB, Bhandari M, Stephen D et al (2010) Development of the radiographic union score for tibial fractures for the assessment of tibial fracture healing after intramedullary fixation. J Trauma 68:629–632. https://doi.org/10.1097/TA.0b013e3181a7c16d. (PMID: 10.1097/TA.0b013e3181a7c16d19996801)
Karnuta JM, Navarro SM, Haeberle HS et al (2019) Bundled care for hip fractures: a machine-learning approach to an untenable patient-specific payment model. J Orthop Trauma 33:324–330. https://doi.org/10.1097/BOT.0000000000001454. (PMID: 10.1097/BOT.000000000000145430730360)
Navarro SM, Wang EY, Haeberle HS et al (2018) Machine learning and primary total knee arthroplasty: patient forecasting for a patient-specific payment model. J Arthroplasty 33:3617–3623. https://doi.org/10.1016/j.arth.2018.08.028. (PMID: 10.1016/j.arth.2018.08.02830243882)
Ramkumar PN, Haeberle HS, Bloomfield MR et al (2019) Artificial intelligence and arthroplasty at a single institution: real-world applications of machine learning to big data, value-based care, mobile health, and remote patient monitoring. J Arthroplasty 34:2204–2209. https://doi.org/10.1016/j.arth.2019.06.018. (PMID: 10.1016/j.arth.2019.06.01831280916)
Ramkumar PN, Navarro SM, Haeberle HS et al (2019) Development and validation of a machine learning algorithm after primary total hip arthroplasty: applications to length of stay and payment models. J Arthroplasty 34:632–637. https://doi.org/10.1016/j.arth.2018.12.030. (PMID: 10.1016/j.arth.2018.12.03030665831)
Beard DJ, Harris K, Dawson J et al (2015) Meaningful changes for the Oxford hip and knee scores after joint replacement surgery. J Clin Epidemiol 68:73–79. https://doi.org/10.1016/j.jclinepi.2014.08.009. (PMID: 10.1016/j.jclinepi.2014.08.009254417004270450)
Fontana MA, Lyman S, Sarker GK et al (2019) Can machine learning algorithms predict which patients Will achieve minimally clinically important differences from total joint arthroplasty? Clin Orthop Relat Res 477:1267–1279. https://doi.org/10.1097/CORR.0000000000000687. (PMID: 10.1097/CORR.0000000000000687310948336554103)
Keurentjes JC, Van Tol FR, Fiocco M et al (2012) Minimal clinically important differences in health-related quality of life after total hip or knee replacement: a systematic review. Bone Joint Res 1:71–77. https://doi.org/10.1302/2046-3758.15.2000065. (PMID: 10.1302/2046-3758.15.2000065236106743626243)
Rupp M, Walter N, Pfeifer C et al (2021) Inzidenz von Frakturen in der Ewachsenenpopulation in Deutschland. Dtsch Arztebl Int 40:665–669.
McCarthy J, Minsky ML, Rochester N et al (1956) A proposal for the Dartmouth summer research project on artificial intelligence. Dartmouth Conference. Dartmouth College, Hanover, New Hampshire.
Shortliffe E (1976) Computer-based medical consultations: MYCIN. Elsevier https://doi.org/10.1016/B978-0-444-00179-5.X5001-X. (PMID: 10.1016/B978-0-444-00179-5.X5001-X)
Lalehzarian SP, Gowd AK, Liu JN (2021) Machine learning in orthopaedic surgery. World J Orthop 12:685–699. https://doi.org/10.5312/wjo.v12.i9.685. (PMID: 10.5312/wjo.v12.i9.685346314528472446)
Olczak J, Pavlopoulos J, Prijs J et al (2021) Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal. Acta Orthop 92:513–525. https://doi.org/10.1080/17453674.2021.1918389. (PMID: 10.1080/17453674.2021.1918389339880818519529)
Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349:255–260. https://doi.org/10.1126/science.aaa8415. (PMID: 10.1126/science.aaa841526185243)
Campbell M, Hoane AJ Jr, Hsu F (2002) Deep blue. Artif Intell 134:57–83. https://doi.org/10.1016/S0004-3702(01)00129-1. (PMID: 10.1016/S0004-3702(01)00129-1)
Ferrucci D, Brown E, Chu-Carroll J et al (2010) Building watson: an overview of the deepQA project. Artif Intell Mag 31(3):59–79. https://doi.org/10.1609/aimag.v31i3.2303. (PMID: 10.1609/aimag.v31i3.2303)
Silver D, Huang A, Maddison CJ et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529:484–489. https://doi.org/10.1038/nature16961. (PMID: 10.1038/nature1696126819042)
Wyatt JM, Booth GJ, Goldman AH (2021) Natural language processing and its use in orthopaedic research. Curr Rev Musculoskelet Med 14:392–396. https://doi.org/10.1007/s12178-021-09734-3. (PMID: 10.1007/s12178-021-09734-3347552768577962)
Thirukumaran CP, Zaman A, Rubery PT et al (2019) Natural language processing for the identification of surgical site infections in orthopaedics. J Bone Joint Surg Am 101:2167–2174. https://doi.org/10.2106/JBJS.19.00661. (PMID: 10.2106/JBJS.19.0066131596819)
Phung VH, Rhee EJ (2019) A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Appl Sci. https://doi.org/10.3390/app9214500. (PMID: 10.3390/app9214500)
Myers TG, Ramkumar PN, Ricciardi BF et al (2020) Artificial intelligence and orthopaedics: an introduction for clinicians. J Bone Joint Surg Am 102:830–840. https://doi.org/10.2106/JBJS.19.01128. (PMID: 10.2106/JBJS.19.0112832379124)
Geron A (2019) Hands-on machine learning with Scikit—learn, Keras and tensor flow. O’Reilly Media.
Martins LF (2014) IPython notebook essentials. Packt.
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003. (PMID: 10.1016/j.neunet.2014.09.00325462637)
Zhao W, Davis CE (2011) A modified artificial immune system based pattern recognition approach—an application to clinical diagnostics. Artif Intell Med 52:1–9. https://doi.org/10.1016/j.artmed.2011.03.001. (PMID: 10.1016/j.artmed.2011.03.001215150333108456)
Kohonen T (2006) Self-organizing neural projections. Neural Netw 19:723–733. https://doi.org/10.1016/j.neunet.2006.05.001. (PMID: 10.1016/j.neunet.2006.05.00116774731)
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539. (PMID: 10.1038/nature1453926017442)
Borjali A, Chen AF, Muratoglu OK et al (2020) Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network. J Orthop Res 38:1465–1471. https://doi.org/10.1002/jor.24617. (PMID: 10.1002/jor.2461731997411)
Erne F, Dehncke D, Herath SC et al (2021) Deep learning in the detection of rare fractures—development of a “deep learning convolutional network” model for detecting Acetabular fractures. Z Orthop Unfall. https://doi.org/10.1055/a-1511-8595. (PMID: 10.1055/a-1511-859534359091)
Olczak J, Emilson F, Razavian A et al (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 92:102–108. https://doi.org/10.1080/17453674.2020.1837420. (PMID: 10.1080/17453674.2020.183742033103536)
Kruse C, Eiken P, Vestergaard P (2017) Machine learning principles can improve hip fracture prediction. Calcif Tissue Int 100:348–360. https://doi.org/10.1007/s00223-017-0238-7. (PMID: 10.1007/s00223-017-0238-728197643)
Xue Y, Zhang R, Deng Y et al (2017) A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PLoS ONE 12:e178992. https://doi.org/10.1371/journal.pone.0178992. (PMID: 10.1371/journal.pone.0178992285750705456368)
Begg R, Kamruzzaman J (2005) A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech 38:401–408. https://doi.org/10.1016/j.jbiomech.2004.05.002. (PMID: 10.1016/j.jbiomech.2004.05.00215652537)
Joyseeree R, Abou Sabha R, Mueller H (2015) Applying machine learning to gait analysis data for disease identification. Stud Health Technol Inform 210:850–854. (PMID: 25991275)
Sikka RS, Baer M, Raja A et al (2019) Analytics in sports medicine: implications and responsibilities that accompany the era of big data. J Bone Joint Surg Am 101:276–283. https://doi.org/10.2106/JBJS.17.01601. (PMID: 10.2106/JBJS.17.0160130730488)
Ekegren CL, Edwards ER, de Steiger R et al (2018) Incidence, costs and predictors of non-union, delayed union and mal-union following long bone fracture. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph15122845. (PMID: 10.3390/ijerph15122845305516326313538)
McCoy TH Jr., Fragomen AT, Hart KL et al (2019) Genomewide association study of fracture nonunion using electronic health records. JBMR Plus 3:23–28. https://doi.org/10.1002/jbm4.10063. (PMID: 10.1002/jbm4.1006330680360)
Mills LA, Aitken SA, Simpson A (2017) The risk of non-union per fracture: current myths and revised figures from a population of over 4 million adults. Acta Orthop 88:434–439. https://doi.org/10.1080/17453674.2017.1321351. (PMID: 10.1080/17453674.2017.1321351285086825499337)
Calori GM, Colombo M, Mazza EL et al (2014) Validation of the non-union scoring system in 300 long bone non-unions. Injury 45(Suppl 6):S93–S97. https://doi.org/10.1016/j.injury.2014.10.030. (PMID: 10.1016/j.injury.2014.10.03025457326)
Calori GM, Phillips M, Jeetle S et al (2008) Classification of non-union: need for a new scoring system? Injury 39(Suppl 2):S59–S63. https://doi.org/10.1016/S0020-1383(08)70016-0. (PMID: 10.1016/S0020-1383(08)70016-018804575)
Santolini E, West RM, Giannoudis PV (2020) Leeds-Genoa Non-Union Index: a clinical tool for asessing the need for early intervention after long bone fracture fixation. Int Orthop 44:161–172. https://doi.org/10.1007/s00264-019-04376-0. (PMID: 10.1007/s00264-019-04376-031440889)
Whelan DB, Bhandari M, Stephen D et al (2010) Development of the radiographic union score for tibial fractures for the assessment of tibial fracture healing after intramedullary fixation. J Trauma 68:629–632. https://doi.org/10.1097/TA.0b013e3181a7c16d. (PMID: 10.1097/TA.0b013e3181a7c16d19996801)
Karnuta JM, Navarro SM, Haeberle HS et al (2019) Bundled care for hip fractures: a machine-learning approach to an untenable patient-specific payment model. J Orthop Trauma 33:324–330. https://doi.org/10.1097/BOT.0000000000001454. (PMID: 10.1097/BOT.000000000000145430730360)
Navarro SM, Wang EY, Haeberle HS et al (2018) Machine learning and primary total knee arthroplasty: patient forecasting for a patient-specific payment model. J Arthroplasty 33:3617–3623. https://doi.org/10.1016/j.arth.2018.08.028. (PMID: 10.1016/j.arth.2018.08.02830243882)
Ramkumar PN, Haeberle HS, Bloomfield MR et al (2019) Artificial intelligence and arthroplasty at a single institution: real-world applications of machine learning to big data, value-based care, mobile health, and remote patient monitoring. J Arthroplasty 34:2204–2209. https://doi.org/10.1016/j.arth.2019.06.018. (PMID: 10.1016/j.arth.2019.06.01831280916)
Ramkumar PN, Navarro SM, Haeberle HS et al (2019) Development and validation of a machine learning algorithm after primary total hip arthroplasty: applications to length of stay and payment models. J Arthroplasty 34:632–637. https://doi.org/10.1016/j.arth.2018.12.030. (PMID: 10.1016/j.arth.2018.12.03030665831)
Beard DJ, Harris K, Dawson J et al (2015) Meaningful changes for the Oxford hip and knee scores after joint replacement surgery. J Clin Epidemiol 68:73–79. https://doi.org/10.1016/j.jclinepi.2014.08.009. (PMID: 10.1016/j.jclinepi.2014.08.009254417004270450)
Fontana MA, Lyman S, Sarker GK et al (2019) Can machine learning algorithms predict which patients Will achieve minimally clinically important differences from total joint arthroplasty? Clin Orthop Relat Res 477:1267–1279. https://doi.org/10.1097/CORR.0000000000000687. (PMID: 10.1097/CORR.0000000000000687310948336554103)
Keurentjes JC, Van Tol FR, Fiocco M et al (2012) Minimal clinically important differences in health-related quality of life after total hip or knee replacement: a systematic review. Bone Joint Res 1:71–77. https://doi.org/10.1302/2046-3758.15.2000065. (PMID: 10.1302/2046-3758.15.2000065236106743626243)
Rupp M, Walter N, Pfeifer C et al (2021) Inzidenz von Frakturen in der Ewachsenenpopulation in Deutschland. Dtsch Arztebl Int 40:665–669.
Contributed Indexing:
Keywords: Cost analysis; Data science; Fracture healing; Personalized medicine; Prediction models
Local Abstract: [Publisher, German] Methoden der künstlichen Intelligenz (KI) haben in den letzten Jahren zunehmend Einzug in die Medizin gefunden. Einige Fachbereiche nutzen diese schon regelmäßig im klinischen Alltag. Die Anwendungsfelder sind weit, aber bisher noch nicht ausgeschöpft und in ihrer Vielfalt nicht ausreichend verstanden. Dieser Übersichtsbeitrag gibt einen Einblick in die Historie der KI und definiert die unterschiedlichen Begrifflichkeiten und Bereiche wie maschinelles Lernen (ML), neuronale Netze oder Deep Learning. Es werden die klassischen Schritte zur Entwicklung eines KI-Modells demonstriert sowie der Kreislauf der Datenbereinigung, -vorbereitung, das Training eines Modells bis hin zur Validierung und Umsetzung in der Praxis des klinischen Alltags erklärt. Bisherige Anwendungsfelder im muskuloskeletalen Fachbereich nutzen sowohl Methoden des ML als auch neuronaler Netze, z. B. zur Identifikation von Frakturen oder zur Klassifizierung. Prädikative Modelle anhand von Risikofaktoren mit dem Ziel der Komplikationsprävention finden erste Anwendung. Da Pseudarthrosen ein zwar seltenes, aber komplexes Krankheitsbild mit soziökonomischer Tragweite darstellen, ergeben sich viele noch offene Fragestellungen, die mithilfe der Methoden der KI zukünftig beantwortet werden könnten. Neue Forschungsfelder unter Nutzung von KI reichen von Prädikationsmodellen über Kostenanalysen bis hin zu personalisierter Therapie.
Local Abstract: [Publisher, German] Methoden der künstlichen Intelligenz (KI) haben in den letzten Jahren zunehmend Einzug in die Medizin gefunden. Einige Fachbereiche nutzen diese schon regelmäßig im klinischen Alltag. Die Anwendungsfelder sind weit, aber bisher noch nicht ausgeschöpft und in ihrer Vielfalt nicht ausreichend verstanden. Dieser Übersichtsbeitrag gibt einen Einblick in die Historie der KI und definiert die unterschiedlichen Begrifflichkeiten und Bereiche wie maschinelles Lernen (ML), neuronale Netze oder Deep Learning. Es werden die klassischen Schritte zur Entwicklung eines KI-Modells demonstriert sowie der Kreislauf der Datenbereinigung, -vorbereitung, das Training eines Modells bis hin zur Validierung und Umsetzung in der Praxis des klinischen Alltags erklärt. Bisherige Anwendungsfelder im muskuloskeletalen Fachbereich nutzen sowohl Methoden des ML als auch neuronaler Netze, z. B. zur Identifikation von Frakturen oder zur Klassifizierung. Prädikative Modelle anhand von Risikofaktoren mit dem Ziel der Komplikationsprävention finden erste Anwendung. Da Pseudarthrosen ein zwar seltenes, aber komplexes Krankheitsbild mit soziökonomischer Tragweite darstellen, ergeben sich viele noch offene Fragestellungen, die mithilfe der Methoden der KI zukünftig beantwortet werden könnten. Neue Forschungsfelder unter Nutzung von KI reichen von Prädikationsmodellen über Kostenanalysen bis hin zu personalisierter Therapie.
Entry Date(s):
Date Created: 20220709 Date Completed: 20220805 Latest Revision: 20220805
Update Code:
20250114
DOI:
10.1007/s00113-022-01202-y
PMID:
35810261
Database:
MEDLINE
Weitere Informationen
Methods of artificial intelligence (AI) have found applications in many fields of medicine within the last few years. Some disciplines already use these methods regularly within their clinical routine. However, the fields of application are wide and there are still many opportunities to apply these new AI concepts. This review article gives an insight into the history of AI and defines the special terms and fields, such as machine learning (ML), neural networks and deep learning. The classical steps in developing AI models are demonstrated here, as well as the iteration of data rectification and preparation, the training of a model and subsequent validation before transfer into a clinical setting are explained. Currently, musculoskeletal disciplines implement methods of ML and also neural networks, e.g. for identification of fractures or for classifications. Also, predictive models based on risk factor analysis for prevention of complications are being initiated. As non-union in bone is a rare but very complex disease with dramatic socioeconomic impact for the healthcare system, many open questions arise which could be better understood by using methods of AI in the future. New fields of research applying AI models range from predictive models and cost analysis to personalized treatment strategies.
(© 2022. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)