An artificial intelligence based on a convolutional neural network allows a precise analysis of the alignment of the lower limb

Purpose The objective of this study was to develop a numeric tool to automate the analysis of deformity from lower limb telemetry and assess its accuracy. Our hypothesis was that artificial intelligence (AI) algorithm would be able to determine mechanical and anatomical angles to within 1°. Methods...

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Veröffentlicht in:International orthopaedics 2023-02, Vol.47 (2), p.511-518
Hauptverfasser: Bernard de Villeneuve, Florent, Jacquet, Christophe, El Kadim, Bilal, Donnez, Mathias, Coue, Olivier, Poujade, Thibault, Khakha, Raghbir, Argenson, Jean-Noel, Ollivier, Matthieu
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container_end_page 518
container_issue 2
container_start_page 511
container_title International orthopaedics
container_volume 47
creator Bernard de Villeneuve, Florent
Jacquet, Christophe
El Kadim, Bilal
Donnez, Mathias
Coue, Olivier
Poujade, Thibault
Khakha, Raghbir
Argenson, Jean-Noel
Ollivier, Matthieu
description Purpose The objective of this study was to develop a numeric tool to automate the analysis of deformity from lower limb telemetry and assess its accuracy. Our hypothesis was that artificial intelligence (AI) algorithm would be able to determine mechanical and anatomical angles to within 1°. Methods After institutional review board approval, 1175 anonymized patient telemetries were extracted from a database of more than ten thousand telemetries. From this selection, 31 packs of telemetries were composed and sent to 11 orthopaedic surgeons for analysis. Each surgeon had to identify on the telemetries fourteen landmarks allowing determination of the following four angles: hip-knee-ankle angle (HKA), medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), and joint line convergence angle (JLCA). An algorithm based on a machine learning process was trained on our database to automatically determine angles. The reliability of the algorithm was evaluated by calculating the difference of determination precision between the surgeons and the algorithm. Results The analysis time for obtaining 28 points and 8 angles per image was 48 ± 12 s for the algorithm. The average difference between the angles measured by the surgeons and the algorithm was around 1.9° for all the angles of interest: 1.3° for HKA, 1.6° for MPTA, 2.1° for LDFA, and 2.4° for JLCA. Intraclass correlation was greater than 95% for all angles. Conclusion The algorithm showed high accuracy for automated angle measurement, allowing the estimation of limb frontal alignment to the nearest degree.
doi_str_mv 10.1007/s00264-022-05634-4
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Our hypothesis was that artificial intelligence (AI) algorithm would be able to determine mechanical and anatomical angles to within 1°. Methods After institutional review board approval, 1175 anonymized patient telemetries were extracted from a database of more than ten thousand telemetries. From this selection, 31 packs of telemetries were composed and sent to 11 orthopaedic surgeons for analysis. Each surgeon had to identify on the telemetries fourteen landmarks allowing determination of the following four angles: hip-knee-ankle angle (HKA), medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), and joint line convergence angle (JLCA). An algorithm based on a machine learning process was trained on our database to automatically determine angles. The reliability of the algorithm was evaluated by calculating the difference of determination precision between the surgeons and the algorithm. Results The analysis time for obtaining 28 points and 8 angles per image was 48 ± 12 s for the algorithm. The average difference between the angles measured by the surgeons and the algorithm was around 1.9° for all the angles of interest: 1.3° for HKA, 1.6° for MPTA, 2.1° for LDFA, and 2.4° for JLCA. Intraclass correlation was greater than 95% for all angles. Conclusion The algorithm showed high accuracy for automated angle measurement, allowing the estimation of limb frontal alignment to the nearest degree.</description><identifier>ISSN: 0341-2695</identifier><identifier>EISSN: 1432-5195</identifier><identifier>DOI: 10.1007/s00264-022-05634-4</identifier><identifier>PMID: 36418444</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Artificial Intelligence ; Humans ; Knee Joint - diagnostic imaging ; Knee Joint - surgery ; Lower Extremity - surgery ; Medicine ; Medicine &amp; Public Health ; Neural Networks, Computer ; Original Paper ; Orthopedics ; Osteoarthritis, Knee - surgery ; Reproducibility of Results ; Retrospective Studies ; Tibia - surgery</subject><ispartof>International orthopaedics, 2023-02, Vol.47 (2), p.511-518</ispartof><rights>The Author(s) under exclusive licence to SICOT aisbl 2022. 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Results The analysis time for obtaining 28 points and 8 angles per image was 48 ± 12 s for the algorithm. The average difference between the angles measured by the surgeons and the algorithm was around 1.9° for all the angles of interest: 1.3° for HKA, 1.6° for MPTA, 2.1° for LDFA, and 2.4° for JLCA. Intraclass correlation was greater than 95% for all angles. 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source MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; SpringerLink Journals - AutoHoldings
subjects Artificial Intelligence
Humans
Knee Joint - diagnostic imaging
Knee Joint - surgery
Lower Extremity - surgery
Medicine
Medicine & Public Health
Neural Networks, Computer
Original Paper
Orthopedics
Osteoarthritis, Knee - surgery
Reproducibility of Results
Retrospective Studies
Tibia - surgery
title An artificial intelligence based on a convolutional neural network allows a precise analysis of the alignment of the lower limb
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