Computer-Aided Diagnosis of Vertebral Compression Fractures Using Convolutional Neural Networks and Radiomics

Lama, Rafael Silva Del; Candido, Raquel; Chiari-Correia, Natália Santana; Nogueira-Barbosa, Marcello Henrique; Azevedo-Marques, Paulo Mazzoncini de; Tinós, Renato

doi:10.1007/s10278-022-00586-y

Cited by 14 publications

(13 citation statements)

References 21 publications

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“…Of the 57 studies using imaging-related and combined data, 33 analyzed radiograph images [ 6 , 7 , 28 – 31 , 35 – 38 , 40 – 42 , 45 , 47 – 49 , 52 – 57 , 59 , 61 , 62 , 66 – 68 , 72 – 74 , 78 ], 12 analyzed computed tomography (CT) images [ 8 , 9 , 39 , 43 , 50 , 63 , 65 , 69 , 75 , 81 – 83 ], and the remaining studies analyzed other imaging modalities ( S1 Table , and S2 Table ). The most common fracture outcome was vertebral fracture (20 studies) [ 8 , 10 , 11 , 28 , 31 , 34 , 35 , 38 , 44 , 46 , 50 , 51 , 58 , 59 , 65 , 72 , 77 , 80 ,…”

Section: Resultsmentioning

confidence: 99%

“…A different set of split samples was utilized, e.g., 80% training and 20% testing [ 10 , 28 , 47 , 57 , 71 ], 90% training and 10% testing [ 32 , 33 , 56 , 81 ], and 80% training, 10% validation, and 10% testing [ 40 , 41 , 65 , 69 ]. Twenty studies reported the cross-validation with 20-folds [ 66 ], 10-folds [ 8 , 14 , 33 , 34 , 39 , 45 , 50 , 53 , 57 , 64 , 72 , 76 , 80 , 81 ], 5-folds [ 13 , 28 , 32 , 38 , 44 , 46 , 48 , 67 , 74 , 78 , 79 ], and 7-folds [ 83 ]. Thirteen studies performed an out-of-sample external validation [ 6 , 7 , 29 – 31 , 35 , 47 , 49 , 56 , 59 , 62 , 72 , 74 ].…”

Section: Resultsmentioning

confidence: 99%

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Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis

Jung,

Dai,

Liu

et al. 2024

PLOS Digit Health

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Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87–96, p< 0.01) and specificity (90%; 95% CI: 85–93, p< 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90–94, p< 0.01; and 91%; 95% CI: 88–93, p < 0.01) were higher than those using tabular data (81%; 95% CI: 77–85, p< 0.01; and 83%; 95% CI: 76–88, p < 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90–96, p < 0.01) and specificity (92%; 95% CI: 89–94, p< 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359).

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis

Jung,

Dai,

Liu

et al. 2024

PLOS Digit Health

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“…Yabu et al ( 63 ) described AI enabled detection of fresh VF on MR images. Del Lama et al ( 64 ) described AI enabled detection of CVFs on spine MRI images. Yeh et al ( 65 ) described an AI enabled model for the diagnosis of VFs on spine MR images.…”

Section: Discussionmentioning

confidence: 99%

A software program for automated compressive vertebral fracture detection on elderly women’s lateral chest radiograph: Ofeye 1.0

Xiao¹,

Zhu²,

Du³

et al. 2022

Quant Imaging Med Surg

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Background Because osteoporotic vertebral fracture (OVF) on chest radiographs is commonly missed in radiological reports, we aimed to develop a software program which offers automated detection of compressive vertebral fracture (CVF) on lateral chest radiographs, and which emphasizes CVF detection specificity with a low false positivity rate. Methods For model training, we retrieved 3,991 spine radiograph cases and 1,979 chest radiograph cases from 16 sources, with among them in total 1,404 cases had OVF. For model testing, we retrieved 542 chest radiograph cases and 162 spine radiograph cases from four independent clinics, with among them 215 cases had OVF. All cases were female subjects, and except for 31 training data cases which were spine trauma cases, all the remaining cases were post-menopausal women. Image data included DICOM (Digital Imaging and Communications in Medicine) format, hard film scanned PNG (Portable Network Graphics) format, DICOM exported PNG format, and PACS (Picture Archiving and Communication System) downloaded resolution reduced DICOM format. OVF classification included: minimal and mild grades with <20% or ≥20–25% vertebral height loss respectively, moderate grade with ≥25–40% vertebral height loss, severe grade with ≥40%–2/3 vertebral height loss, and collapsed grade with ≥2/3 vertebral height loss. The CVF detection base model was mainly composed of convolution layers that include convolution kernels of different sizes, pooling layers, up-sampling layers, feature merging layers, and residual modules. When the model loss function could not be further decreased with additional training, the model was considered to be optimal and termed ‘base-model 1.0’. A user-friendly interface was also developed, with the synthesized software termed ‘Ofeye 1.0’. Results Counting cases and with minimal and mild OVFs included, base-model 1.0 demonstrated a specificity of 97.1%, a sensitivity of 86%, and an accuracy of 93.9% for the 704 testing cases. In total, 33 OVFs in 30 cases had a false negative reading, which constituted a false negative rate of 14.0% (30/215) by counting all OVF cases. Eighteen OVFs in 15 cases had OVFs of ≥ moderate grades missed, which constituted a false negative rate of 7.0% (15/215, i.e., sensitivity 93%) if only counting cases with ≥ moderate grade OVFs missed. False positive reading was recorded in 13 vertebrae in 13 cases (one vertebra in each case), which constituted a false positivity rate of 2.7% (13/489). These vertebrae with false positivity labeling could be readily differentiated from a true OVF by a human reader. The software Ofeye 1.0 allows ‘batch processing’, for example, 100 radiographs can be processed in a single operation. This software can be integrated into hospital PACS, or installed in a standalone personal computer. Conclusions A user-friendly software program was developed for CVF detection on elderly women’s lateral chest radiographs. It has an o...

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“…According to the nature of the disease, it can be divided into benign and malignant VCFs. Benign VCFs are often caused by osteoporosis or trauma, while malignant VCFs are often caused by tumors, such as metastatic, myeloma, lymphoma, langerhans cell histiocytosis (LCH), etc [1][2][3]5]. Osteoporosis is the predominant cause of benign VCFs that can disrupt the structure of the bone trabeculae in the vertebrae and change the contents of the non-collagenous proteins in the bone's matrix [6,7].…”

Section: Introductionmentioning

confidence: 99%

Value of 18F-FDG-PET/CT radiomics combined clinical variables in differential diagnosis of malignant and benign vertebral compression fractures

Zhou

et al. 2023

Preprint

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Background Vertebral compression fractures (VCFs) are common clinical problems that can be caused by various reasons. The differential diagnosis of benign and malignant VCFs is challengeable. This study aimed to develop and validate a radiomics model to predict benign and malignant VCFs on 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT). Results Twenty-six features (9 PET features and 17 CT features) and five clinical variables (age, SUVmax, SUVpeak, SULmax, SULpeak) were selected ultimately. The area under the curve (AUC) of the radiomics and clinical-radiomics model were statistically different from the clinical model in both training group (0.986, 0.987 vs. 0.884, p < 0.05) and test group (0.962, 0.948 vs. 0.858, p < 0.05), while there was no significant difference between the radiomics model and clinical-radiomics model (p > 0.05). The accuracy of the radiomics and clinical-radiomics model was 94%, 95.0% in training group and 93.2%, 93.2% in test group. Three models all showed good calibration (Hosmer_Lemeshow_test, p > 0.05). According to the decision curve analysis (DCA), the radiomics model and clinical-radiomics model showed higher overall net benefit than the clinical model. Conclusions The PET/CT-based radiomics and clinical-radiomics model showed good performance in distinguishing between malignant and benign VCFs. The radiomics method may be valuable for treatment decision-making.

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Computer-Aided Diagnosis of Vertebral Compression Fractures Using Convolutional Neural Networks and Radiomics

Cited by 14 publications

References 21 publications

Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis

Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis

A software program for automated compressive vertebral fracture detection on elderly women’s lateral chest radiograph: Ofeye 1.0

Value of 18F-FDG-PET/CT radiomics combined clinical variables in differential diagnosis of malignant and benign vertebral compression fractures

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