Comparison of different machine learning approaches to detect femoral neck fractures in x-ray images

Açıcı, Koray; Sümer, Emre; Beyaz, Salih

doi:10.1007/s12553-021-00543-9

Cited by 12 publications

(7 citation statements)

References 36 publications

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“…Of 39 studies that met all criteria and were included in this analysis, 18 studies (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 studies (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A PRISMA flowchart of included studies is displayed in eFigure 1 in Supplement 1.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, the meta-analysis conducted to compare the accuracy of these ML models revealed that the models are comparable with the mean performance of expert clinicians at diagnosing hip fractures. Across all included studies, there was a wider range of sensitivity and specificity compared with clinician performance. However, the range was negatively skewed by 1 study that attempted to classify fractures into 3 different categories despite a relatively small training sample size.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence for Hip Fracture Detection and Outcome Prediction

et al. 2023

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ImportanceArtificial intelligence (AI) enables powerful models for establishment of clinical diagnostic and prognostic tools for hip fractures; however the performance and potential impact of these newly developed algorithms are currently unknown.ObjectiveTo evaluate the performance of AI algorithms designed to diagnose hip fractures on radiographs and predict postoperative clinical outcomes following hip fracture surgery relative to current practices.Data SourcesA systematic review of the literature was performed using the MEDLINE, Embase, and Cochrane Library databases for all articles published from database inception to January 23, 2023. A manual reference search of included articles was also undertaken to identify any additional relevant articles.Study SelectionStudies developing machine learning (ML) models for the diagnosis of hip fractures from hip or pelvic radiographs or to predict any postoperative patient outcome following hip fracture surgery were included.Data Extraction and SynthesisThis study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and was registered with PROSPERO. Eligible full-text articles were evaluated and relevant data extracted independently using a template data extraction form. For studies that predicted postoperative outcomes, the performance of traditional predictive statistical models, either multivariable logistic or linear regression, was recorded and compared with the performance of the best ML model on the same out-of-sample data set.Main Outcomes and MeasuresDiagnostic accuracy of AI models was compared with the diagnostic accuracy of expert clinicians using odds ratios (ORs) with 95% CIs. Areas under the curve for postoperative outcome prediction between traditional statistical models (multivariable linear or logistic regression) and ML models were compared.ResultsOf 39 studies that met all criteria and were included in this analysis, 18 (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A total of 39 598 plain radiographs and 714 939 hip fractures were used for training, validating, and testing ML models specific to diagnosis and postoperative outcome prediction, respectively. Mortality and length of hospital stay were the most predicted outcomes. On pooled data analysis, compared with clinicians, the OR for diagnostic error of ML models was 0.79 (95% CI, 0.48-1.31; P = .36; I2 = 60%) for hip fracture radiographs. For the ML models, the mean (SD) sensitivity was 89.3% (8.5%), specificity was 87.5% (9.9%), and F1 score was 0.90 (0.06). The mean area under the curve for mortality prediction was 0.84 with ML models compared with 0.79 for alternative controls (P = .09).Conclusions and RelevanceThe findings of this systematic review and meta-analysis suggest that the potential applications of AI to aid with diagnosis from hip radiographs are promising. The performance of AI in diagnosing hip fractures was comparable with that of expert radiologists and surgeons. However, current implementations of AI for outcome prediction do not seem to provide substantial benefit over traditional multivariable predictive statistics.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence for Hip Fracture Detection and Outcome Prediction

et al. 2023

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“…[19] There are successful clinical studies evaluating changes in the skeletal system due to trauma or osteoarthritis with DCNN technology [2,[20][21][22][23] which has been used in computer plain imaging, particularly in the field of medical radiology. [24][25][26][27][28][29][30][31] Accordingly, this method has become increasingly popular in musculoskeletal radiology. Recently, Al Arif et al [32] defined a fully automatic cervical vertebra segmentation framework for radiographic images in cervical trauma patients.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods

et al. 2022

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Although the cervical spine supports the head, it has quite functional features such as flexion, extension, lateral flexion, and rotation. Due to this wide range of motion, there is a susceptibility to trauma and degenerative changes. Neck pain due to cervical spine problems is a very common medical problem in the adult population. Cervical strain, cervical spondylosis, and discopathies are common causes of mechanical neck pain. Apart from these, traumas, tumors and reflected pain are also important causes of neck pain. [1] Patients with non-traumatic neck pain can be examined in various polyclinics such as family medicine, internal medicine, orthopedics, physical medicine and rehabilitation, neurology, neurosurgery, and rheumatology outpatient clinics. Almost every patient is evaluated with plain radiography in terms Objectives: In this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology. Materials and methods:In this retrospective study, the convolutional neural networks were used and transfer learning method was applied with the pre-trained VGG-16, VGG-19, Resnet-101, and DenseNet-201 networks. Our data set consisted of 161 normal lateral cervical radiographs and 170 lateral cervical radiographs with osteoarthritis and cervical degenerative disc disease. Results:We compared the performances of the classification models in terms of performance metrics such as accuracy, sensitivity, specificity, and precision metrics. Pre-trained VGG-16 network outperformed other models in terms of accuracy (93.9%), sensitivity (95.8%), specificity (92.0%), and precision (92.0%) results. Conclusion:The results of this study suggest that the deep learning methods are promising support tool in automated control of cervical graphs using the DCNN and the exclusion of normal graphs. Such a supportive tool may reduce the diagnosis time and provide radiologists or clinicians to have more time to interpret abnormal graphs.

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“…Convolutional neural networks [ 1 ] (CNN) have been developed specifically for learning the pattern of data on 2-dimensional data and making use of it. This process can also be used with one-dimensional data collected during the walking activity of the patients.…”

Section: Methodsmentioning

confidence: 99%

Neurodegenerative diseases detection and grading using gait dynamics

Erdaş

Sümer

Kibaroğlu

2023

Multimed Tools Appl

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Detection of neurodegenerative diseases such as Parkinson’s disease, Huntington’s disease, Amyotrophic Lateral Sclerosis, and grading of these diseases’ severity have high clinical significance. These tasks based on walking analysis stand out compared to other methods due to their simplicity and non-invasiveness. This study has emerged to realize an artificial intelligence-based disease detection and severity prediction system for neurodegenerative diseases using gait features obtained from gait signals. For the detection of the disease, the problem is divided into parts which are subgroups of 4 classes consisting of Parkinson’s, Huntington’s, Amyotrophic Lateral Sclerosis diseases, and the control group. In addition, the disease vs. control subgroup where all diseases are collected under a single label, the subgroups where each disease is separately against the control group. For disease severity grading, each disease was divided into subgroups and a solution was sought for the prediction problem mentioned by various machine and deep learning methods separately for each group. In this context, the resulting detection performance was measured by the metrics of Accuracy, F 1 Score, Precision, and Recall while the resulting prediction performance was measured by the metrics such as R, R 2 , MAE, MedAE, MSE, and RMSE.

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Comparison of different machine learning approaches to detect femoral neck fractures in x-ray images

Cited by 12 publications

References 36 publications

Artificial Intelligence for Hip Fracture Detection and Outcome Prediction

Artificial Intelligence for Hip Fracture Detection and Outcome Prediction

Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods

Neurodegenerative diseases detection and grading using gait dynamics

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