Artificial intelligence for the detection of vertebral fractures on plain spinal radiography

Murata, Kazuma; Endo, Kenji; Aihara, Takato; Suzuki, Hidekazu; Sawaji, Yasunobu; Matsuoka, Yuji; Nishimura, Hirosuke; Takamatsu, Taichiro; Konishi, Takamitsu; Maekawa, Asato; Yamauchi, Hideya; Kanazawa, Kei; Endo, Hiroo; Tsuji, Hanako; Inoue, Shigeru; Fukushima, Noritoshi; Kikuchi, Hiroyuki; Sato, Hiroki; Yamamoto, Kenjiro

doi:10.1038/s41598-020-76866-w

Cited by 77 publications

(84 citation statements)

References 37 publications

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“…( 77 ) Twelve studies compared ML model performance versus human experts. ( 66,71,78–83,85,91,93,94 ) In four of these studies, ML outperformed human experts significantly. ( 80,83,85,91 ) Thirteen studies applied transfer learning based on pre‐defined CNN architectures, pre‐trained on the ImageNet data set ( 77,79–83,85,90,91,93,95 ) or on a radiography image database.…”

Section: Resultsmentioning

confidence: 99%

“…Among the 32 studies that investigated fracture detection (Table 3), (66–97 ) 11 were on vertebral fractures, ( 66–76 ) 17 hip fractures, ( 74–90 ) and 10 other fracture sites such as humerus or wrist. ( 75,76,90–97 ) Nineteen studies developed CNN models for image analysis.…”

Section: Resultsmentioning

confidence: 99%

“…( 75,76,90–97 ) Nineteen studies developed CNN models for image analysis. ( 66,67,71,72,77–86,90,91,93–95 ) Others used features extracted from images or collected from non‐imaging data. ( 68–70,73–77,87–89,92,96,97 ) Studies reported average best AUC of 0.92 (range 0.63 to 1.00) and average best accuracy of 89.8% (range 78.4% to 99.1%].…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, several studies showed a greater awareness of the potential existence of bias and made efforts to address this either directly by controlling for the risks of overfitting, ( 23,28,30,32,34,37–40,43–46,49,53,54,56,57,59,64,77–85,90,91,93–95 ) indirectly by visualizing decision‐making, ( 36,38,48,66,67,78,79,84,90,94,104 ) or by using gold standard strategies for comparison. ( 66,71,78–83,85,91,93,94,104,107 )…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

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Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high‐dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12‐point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML‐based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model‐based decision‐making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

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“…This study demonstrated that AI-based techniques can detect vertebral fractures on radiographs with very high accuracy. Both models achieved similar sensitivity and specificity to that achieved by expert surgeons and radiologists (26,(33)(34)(35)(36)(37) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)…”

Section: Discussionmentioning

confidence: 92%

Artificial Intelligence Accurately Detects Traumatic Thoracolumbar Fractures on Sagittal Radiographs

Cina

Schirò

et al. 2021

Preprint

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Background context Traumatic thoracolumbar (TL) fractures are frequently encountered in emergency rooms. Sagittal and anteroposterior radiographs are the first step in the trauma routine imaging. Up to 30% of TL fractures are missed in this imaging modality, thus requiring a CT and/or MRI to confirm the diagnosis. A delay in treatment leads to increased morbidity, mortality, exposure to ionizing radiation and financial burden. Fracture detection with Machine Learning models has achieved expert level performance in previous studies. Reliably detecting vertebral fractures in simple radiographic projections would have a significant clinical and financial impact. Purpose To develop a deep learning model that detects traumatic fractures on sagittal radiographs of the TL spine. Study design/setting Retrospective Cohort study. Methods We collected sagittal radiographs, CT and MRI scans of the TL spine of 362 patients exhibiting traumatic vertebral fractures. Cases were excluded when CT and/or MRI where not available. The reference standard was set by an expert group of three spine surgeons who conjointly annotated the sagittal radiographs of 171 cases. CT and/or MRI were reviewed to confirm the presence and type of the fracture in all cases. 302 cropped vertebral images were labelled -fracture- and 328 -no fracture-. After augmentation, this dataset was then used to train, validate, and test deep learning classifiers based on ResNet18 and VGG16 architectures. To ensure that the prediction of the model was based on the correct identification of the fracture zone, an Activation Map analysis was conducted. Results Vertebras T12 to L2 were the most frequently involved, accounting for 48% of the fractures. A4, A3 and A1 were the most frequent AO Spine fracture types. Accuracies of 88% and 84% were obtained with ResNet18 and VGG16 respectively. The sensitivity was 89% with both architectures but ResNet18 showed a higher specificity (88%) compared to VGG16 (79%). The fracture zone was precisely identified in 81% of the heatmaps. Conclusions Our AI model can accurately identify anomalies suggestive of vertebral fractures in sagittal radiographs by precisely identifying the fracture zone within the vertebral body. Clinical significance Clinical implementation of a diagnosis aid tool specifically trained for TL fracture identification is anticipated to reduce the rate of missed vertebral fractures in emergency rooms.

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Deep-Learning-Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X-Ray Radiography

Hong

Cho

Shin

et al. 2020

Journal of Bone and Mineral Research

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Osteoporosis and vertebral fractures (VFs) remain underdiagnosed. The addition of deep learning methods to lateral spine radiography (a simple, widely available, low-cost test) can potentially solve this problem. In this study, we develop deep learning scores to detect osteoporosis and VF based on lateral spine radiography and investigate whether their use can improve referral of high-risk individuals to bone-density testing. The derivation cohort consisted of patients aged 50 years or older who underwent lateral spine radiography in Severance Hospital, Korea, from January 2007 to December 2018, providing a total of 26,299 lateral spine plain X-rays for 9276 patients (VF prevalence, 18.6%; osteoporosis prevalence, 40.3%). Two individual deep convolutional neural network scores to detect prevalent VF (VERTE-X pVF score) and osteoporosis (VERTE-X osteo score) were tested on an internal test set (20% hold-out set) and external test set (another hospital cohort [Yongin], 395 patients). VERTE-X pVF, osteo scores, and clinical models to detect prevalent VF or osteoporosis were compared in terms of the areas under the receiver-operating-characteristics curves (AUROCs). Net reclassification improvement (NRI) was calculated when using deep-learning scores to supplement clinical indications for classification of high-risk individuals to dual-energy X-ray absorptiometry (DXA) testing. VERTE-X pVF and osteo scores outperformed clinical models in both the internal (AUROC: VF, 0.93 versus 0.78; osteoporosis, 0.85 versus 0.79) and external (VF, 0.92 versus 0.79; osteoporosis, 0.83 versus 0.65; p < 0.01 for all) test sets. VERTE-X pVF and osteo scores improved the reclassification of individuals with osteoporosis to the DXA testing group when applied together with the clinical indications for DXA testing in both the internal (NRI 0.10) and external (NRI 0.14, p < 0.001 for all) test sets. The proposed method could detect prevalent VFs and osteoporosis, and it improved referral of individuals at high risk of fracture to DXA testing more than clinical indications alone.

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Artificial intelligence for the detection of vertebral fractures on plain spinal radiography

Cited by 77 publications

References 37 publications

Machine Learning Solutions for Osteoporosis—A Review

Machine Learning Solutions for Osteoporosis—A Review

Artificial Intelligence Accurately Detects Traumatic Thoracolumbar Fractures on Sagittal Radiographs

Deep-Learning-Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X-Ray Radiography

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