Evaluation of a deep learning software for automated measurements on full-leg standing radiographs
Abstract Background Precise lower limb measurements are crucial for assessing musculoskeletal health; fully automated solutions have the potential to enhance standardization and reproducibility of these measurements. This study compared the measurements performed by BoneMetrics (Gleamer, Paris, Fran...
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BMC
2024-11-01
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| Series: | Knee Surgery & Related Research |
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| Online Access: | https://doi.org/10.1186/s43019-024-00246-1 |
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| author | Louis Lassalle Nor-Eddine Regnard Marion Durteste Jeanne Ventre Vincent Marty Lauryane Clovis Zekun Zhang Nicolas Nitche Alexis Ducarouge Jean-Denis Laredo Ali Guermazi |
| author_facet | Louis Lassalle Nor-Eddine Regnard Marion Durteste Jeanne Ventre Vincent Marty Lauryane Clovis Zekun Zhang Nicolas Nitche Alexis Ducarouge Jean-Denis Laredo Ali Guermazi |
| author_sort | Louis Lassalle |
| collection | DOAJ |
| description | Abstract Background Precise lower limb measurements are crucial for assessing musculoskeletal health; fully automated solutions have the potential to enhance standardization and reproducibility of these measurements. This study compared the measurements performed by BoneMetrics (Gleamer, Paris, France), a commercial artificial intelligence (AI)-based software, to expert manual measurements on anteroposterior full-leg standing radiographs. Methods A retrospective analysis was conducted on a dataset comprising consecutive anteroposterior full-leg standing radiographs obtained from four imaging institutions. Key anatomical landmarks to define the hip–knee–ankle angle, pelvic obliquity, leg length, femoral length, and tibial length were annotated independently by two expert musculoskeletal radiologists and served as the ground truth. The performance of the AI was compared against these reference measurements using the mean absolute error, Bland–Altman analyses, and intraclass correlation coefficients. Results A total of 175 anteroposterior full–leg standing radiographs from 167 patients were included in the final dataset (mean age = 49.9 ± 23.6 years old; 103 women and 64 men). Mean absolute error values were 0.30° (95% confidence interval [CI] [0.28, 0.32]) for the hip–knee–ankle angle, 0.75 mm (95% CI [0.60, 0.88]) for pelvic obliquity, 1.03 mm (95% CI [0.91,1.14]) for leg length from the top of the femoral head, 1.45 mm (95% CI [1.33, 1.60]) for leg length from the center of the femoral head, 0.95 mm (95% CI [0.85, 1.04]) for femoral length from the top of the femoral head, 1.23 mm (95% CI [1.12, 1.32]) for femoral length from the center of the femoral head, and 1.38 mm (95% CI [1.21, 1.52]) for tibial length. The Bland–Altman analyses revealed no systematic bias across all measurements. Additionally, the software exhibited excellent agreement with the gold-standard measurements with intraclass correlation coefficient (ICC) values above 0.97 for all parameters. Conclusions Automated measurements on anteroposterior full-leg standing radiographs offer a reliable alternative to manual assessments. The use of AI in musculoskeletal radiology has the potential to support physicians in their daily practice without compromising patient care standards. |
| format | Article |
| id | doaj-art-0bd76bfba8e24f4b871c48825b5b4d3a |
| institution | Kabale University |
| issn | 2234-2451 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Knee Surgery & Related Research |
| spelling | doaj-art-0bd76bfba8e24f4b871c48825b5b4d3a2024-12-01T12:32:03ZengBMCKnee Surgery & Related Research2234-24512024-11-0136111310.1186/s43019-024-00246-1Evaluation of a deep learning software for automated measurements on full-leg standing radiographsLouis Lassalle0Nor-Eddine Regnard1Marion Durteste2Jeanne Ventre3Vincent Marty4Lauryane Clovis5Zekun Zhang6Nicolas Nitche7Alexis Ducarouge8Jean-Denis Laredo9Ali Guermazi10Réseau Imagerie Sud FrancilienRéseau Imagerie Sud FrancilienGleamerGleamerGleamerGleamerGleamerGleamerGleamerGleamerDepartment of Radiology, Boston University School of MedicineAbstract Background Precise lower limb measurements are crucial for assessing musculoskeletal health; fully automated solutions have the potential to enhance standardization and reproducibility of these measurements. This study compared the measurements performed by BoneMetrics (Gleamer, Paris, France), a commercial artificial intelligence (AI)-based software, to expert manual measurements on anteroposterior full-leg standing radiographs. Methods A retrospective analysis was conducted on a dataset comprising consecutive anteroposterior full-leg standing radiographs obtained from four imaging institutions. Key anatomical landmarks to define the hip–knee–ankle angle, pelvic obliquity, leg length, femoral length, and tibial length were annotated independently by two expert musculoskeletal radiologists and served as the ground truth. The performance of the AI was compared against these reference measurements using the mean absolute error, Bland–Altman analyses, and intraclass correlation coefficients. Results A total of 175 anteroposterior full–leg standing radiographs from 167 patients were included in the final dataset (mean age = 49.9 ± 23.6 years old; 103 women and 64 men). Mean absolute error values were 0.30° (95% confidence interval [CI] [0.28, 0.32]) for the hip–knee–ankle angle, 0.75 mm (95% CI [0.60, 0.88]) for pelvic obliquity, 1.03 mm (95% CI [0.91,1.14]) for leg length from the top of the femoral head, 1.45 mm (95% CI [1.33, 1.60]) for leg length from the center of the femoral head, 0.95 mm (95% CI [0.85, 1.04]) for femoral length from the top of the femoral head, 1.23 mm (95% CI [1.12, 1.32]) for femoral length from the center of the femoral head, and 1.38 mm (95% CI [1.21, 1.52]) for tibial length. The Bland–Altman analyses revealed no systematic bias across all measurements. Additionally, the software exhibited excellent agreement with the gold-standard measurements with intraclass correlation coefficient (ICC) values above 0.97 for all parameters. Conclusions Automated measurements on anteroposterior full-leg standing radiographs offer a reliable alternative to manual assessments. The use of AI in musculoskeletal radiology has the potential to support physicians in their daily practice without compromising patient care standards.https://doi.org/10.1186/s43019-024-00246-1Leg measurementsArtificial intelligenceRadiography |
| spellingShingle | Louis Lassalle Nor-Eddine Regnard Marion Durteste Jeanne Ventre Vincent Marty Lauryane Clovis Zekun Zhang Nicolas Nitche Alexis Ducarouge Jean-Denis Laredo Ali Guermazi Evaluation of a deep learning software for automated measurements on full-leg standing radiographs Knee Surgery & Related Research Leg measurements Artificial intelligence Radiography |
| title | Evaluation of a deep learning software for automated measurements on full-leg standing radiographs |
| title_full | Evaluation of a deep learning software for automated measurements on full-leg standing radiographs |
| title_fullStr | Evaluation of a deep learning software for automated measurements on full-leg standing radiographs |
| title_full_unstemmed | Evaluation of a deep learning software for automated measurements on full-leg standing radiographs |
| title_short | Evaluation of a deep learning software for automated measurements on full-leg standing radiographs |
| title_sort | evaluation of a deep learning software for automated measurements on full leg standing radiographs |
| topic | Leg measurements Artificial intelligence Radiography |
| url | https://doi.org/10.1186/s43019-024-00246-1 |
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