1
|
Yıldız Potter İ, Yeritsyan D, Rodriguez EK, Wu JS, Nazarian A, Vaziri A. Detection and Localization of Spine Disorders from Plain Radiography. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01175-x. [PMID: 38937344 DOI: 10.1007/s10278-024-01175-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/16/2024] [Accepted: 06/09/2024] [Indexed: 06/29/2024]
Abstract
Spine disorders can cause severe functional limitations, including back pain, decreased pulmonary function, and increased mortality risk. Plain radiography is the first-line imaging modality to diagnose suspected spine disorders. Nevertheless, radiographical appearance is not always sufficient due to highly variable patient and imaging parameters, which can lead to misdiagnosis or delayed diagnosis. Employing an accurate automated detection model can alleviate the workload of clinical experts, thereby reducing human errors, facilitating earlier detection, and improving diagnostic accuracy. To this end, deep learning-based computer-aided diagnosis (CAD) tools have significantly outperformed the accuracy of traditional CAD software. Motivated by these observations, we proposed a deep learning-based approach for end-to-end detection and localization of spine disorders from plain radiographs. In doing so, we took the first steps in employing state-of-the-art transformer networks to differentiate images of multiple spine disorders from healthy counterparts and localize the identified disorders, focusing on vertebral compression fractures (VCF) and spondylolisthesis due to their high prevalence and potential severity. The VCF dataset comprised 337 images, with VCFs collected from 138 subjects and 624 normal images collected from 337 subjects. The spondylolisthesis dataset comprised 413 images, with spondylolisthesis collected from 336 subjects and 782 normal images collected from 413 subjects. Transformer-based models exhibited 0.97 Area Under the Receiver Operating Characteristic Curve (AUC) in VCF detection and 0.95 AUC in spondylolisthesis detection. Further, transformers demonstrated significant performance improvements against existing end-to-end approaches by 4-14% AUC (p-values < 10-13) for VCF detection and by 14-20% AUC (p-values < 10-9) for spondylolisthesis detection.
Collapse
Affiliation(s)
| | - Diana Yeritsyan
- Beth Israel Deaconess Medical Center (BIDMC), Carl J. Shapiro Department of Orthopedic Surgery, Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA
- Musculoskeletal Translational Innovation Initiative, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, RN123, USA
| | - Edward K Rodriguez
- Beth Israel Deaconess Medical Center (BIDMC), Carl J. Shapiro Department of Orthopedic Surgery, Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA
- Musculoskeletal Translational Innovation Initiative, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, RN123, USA
| | - Jim S Wu
- Department of Radiology, Massachusetts General Brigham (MGB), Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA
| | - Ara Nazarian
- Beth Israel Deaconess Medical Center (BIDMC), Carl J. Shapiro Department of Orthopedic Surgery, Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA
- Musculoskeletal Translational Innovation Initiative, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, RN123, USA
- Department of Orthopaedics Surgery, Yerevan State University, 0025, Yerevan, Armenia
| | - Ashkan Vaziri
- BioSensics, LLC, 57 Chapel Street, Newton, MA, 02458, USA
| |
Collapse
|
2
|
Arana E. Diagnostic imaging in patients with vertebral compression fractures. Rev Esp Cir Ortop Traumatol (Engl Ed) 2024:S1888-4415(24)00093-6. [PMID: 38878884 DOI: 10.1016/j.recot.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 06/03/2024] [Accepted: 06/10/2024] [Indexed: 07/12/2024] Open
Abstract
Vertebral compression fractures by osteoporosis (OVF) is usually a diagnostic problem and coincides on the age group of metastatic vertebral compression fractures (MVF). Although radiography is the first diagnostic technique, generally is not accurate for depicting demineralization and soft tissue lesions. Magnetic resonance (MRI) is the diagnostic choice. The most relevant signs are Intravertebral fluid collection or fluid signal, other vertebral deformities without edema and older age. Among the most relevant findings for diagnosis MVF are soft tissue mass and pedicle intensity signal asymmetries. However, reproducibility of these findings in clinical practice is moderate.
Collapse
Affiliation(s)
- E Arana
- Servicio de Radiodiagnóstico. Fundación IVO, Valencia. Red Española de Investigadores en Dolencias de la Espalda (REIDE), España.
| |
Collapse
|
3
|
Zhang H, Xu R, Guo X, Zhou D, Xu T, Zhong X, Kong M, Zhang Z, Wang Y, Ma X. Deep learning-based automated high-accuracy location and identification of fresh vertebral compression fractures from spinal radiographs: a multicenter cohort study. Front Bioeng Biotechnol 2024; 12:1397003. [PMID: 38812917 PMCID: PMC11135169 DOI: 10.3389/fbioe.2024.1397003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 04/29/2024] [Indexed: 05/31/2024] Open
Abstract
Background Digital radiography (DR) is a common and widely available examination. However, spinal DR cannot detect bone marrow edema, therefore, determining vertebral compression fractures (VCFs), especially fresh VCFs, remains challenging for clinicians. Methods We trained, validated, and externally tested the deep residual network (DRN) model that automated the detection and identification of fresh VCFs from spinal DR images. A total of 1,747 participants from five institutions were enrolled in this study and divided into the training cohort, validation cohort and external test cohorts (YHDH and BMUH cohorts). We evaluated the performance of DRN model based on the area under the receiver operating characteristic curve (AUC), feature attention maps, sensitivity, specificity, and accuracy. We compared it with five other deep learning models and validated and tested the model internally and externally and explored whether it remains highly accurate for an external test cohort. In addition, the influence of old VCFs on the performance of the DRN model was assessed. Results The AUC was 0.99, 0.89, and 0.88 in the validation, YHDH, and BMUH cohorts, respectively, for the DRN model for detecting and discriminating fresh VCFs. The accuracies were 81.45% and 72.90%, sensitivities were 84.75% and 91.43%, and specificities were 80.25% and 63.89% in the YHDH and BMUH cohorts, respectively. The DRN model generated correct activation on the fresh VCFs and accurate peak responses on the area of the target vertebral body parts and demonstrated better feature representation learning and classification performance. The AUC was 0.90 (95% confidence interval [CI] 0.84-0.95) and 0.84 (95% CI 0.72-0.93) in the non-old VCFs and old VCFs groups, respectively, in the YHDH cohort (p = 0.067). The AUC was 0.89 (95% CI 0.84-0.94) and 0.85 (95% CI 0.72-0.95) in the non-old VCFs and old VCFs groups, respectively, in the BMUH cohort (p = 0.051). Conclusion In present study, we developed the DRN model for automated diagnosis and identification of fresh VCFs from spinal DR images. The DRN model can provide interpretable attention maps to support the excellent prediction results, which is the key that most clinicians care about when using the model to assist decision-making.
Collapse
Affiliation(s)
- Hao Zhang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Ruixiang Xu
- Department of Pain, YanTai YuHuangDing Hospital, Yantai, Shandong, China
| | - Xiang Guo
- Department of Spinal Surgery, The Affiliated Hospital of Weifang Medical University, Weifang, Shandong, China
| | - Dan Zhou
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Tongshuai Xu
- Department of Spinal Surgery, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Xin Zhong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Meng Kong
- Department of Spinal Surgery, Qingdao Municipal Hospital, Qingdao, Shandong, China
| | - Zhimin Zhang
- Department of Stomatology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yan Wang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xuexiao Ma
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| |
Collapse
|
4
|
Gong AJ, Fu W, Li H, Guo N, Pan T. A Siamese ResNeXt network for predicting carotid intimal thickness of patients with T2DM from fundus images. Front Endocrinol (Lausanne) 2024; 15:1364519. [PMID: 38549767 PMCID: PMC10973133 DOI: 10.3389/fendo.2024.1364519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 02/21/2024] [Indexed: 04/02/2024] Open
Abstract
Objective To develop and validate an artificial intelligence diagnostic model based on fundus images for predicting Carotid Intima-Media Thickness (CIMT) in individuals with Type 2 Diabetes Mellitus (T2DM). Methods In total, 1236 patients with T2DM who had both retinal fundus images and CIMT ultrasound records within a single hospital stay were enrolled. Data were divided into normal and thickened groups and sent to eight deep learning models: convolutional neural networks of the eight models were all based on ResNet or ResNeXt. Their encoder and decoder modes are different, including the standard mode, the Parallel learning mode, and the Siamese mode. Except for the six unimodal networks, two multimodal networks based on ResNeXt under the Parallel learning mode or the Siamese mode were embedded with ages. Performance of eight models were compared via the confusion matrix, precision, recall, specificity, F1 value, and ROC curve, and recall was regarded as the main indicator. Besides, Grad-CAM was used to visualize the decisions made by Siamese ResNeXt network, which is the best performance. Results Performance of various models demonstrated the following points: 1) the RexNeXt showed a notable improvement over the ResNet; 2) the structural Siamese networks, which extracted features parallelly and independently, exhibited slight performance enhancements compared to the traditional networks. Notably, the Siamese networks resulted in significant improvements; 3) the performance of classification declined if the age factor was embedded in the network. Taken together, the Siamese ResNeXt unimodal model performed best for its superior efficacy and robustness. This model achieved a recall rate of 88.0% and an AUC value of 90.88% in the validation subset. Additionally, heatmaps calculated by the Grad-CAM algorithm presented concentrated and orderly mappings around the optic disc vascular area in normal CIMT groups and dispersed, irregular patterns in thickened CIMT groups. Conclusion We provided a Siamese ResNeXt neural network for predicting the carotid intimal thickness of patients with T2DM from fundus images and confirmed the correlation between fundus microvascular lesions and CIMT.
Collapse
Affiliation(s)
- AJuan Gong
- Department of Endocrinology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wanjin Fu
- Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Heng Li
- The Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Na Guo
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
| | - Tianrong Pan
- Department of Endocrinology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| |
Collapse
|
5
|
Silberstein J, Wee C, Gupta A, Seymour H, Ghotra SS, Sá dos Reis C, Zhang G, Sun Z. Artificial Intelligence-Assisted Detection of Osteoporotic Vertebral Fractures on Lateral Chest Radiographs in Post-Menopausal Women. J Clin Med 2023; 12:7730. [PMID: 38137799 PMCID: PMC10743975 DOI: 10.3390/jcm12247730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/06/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
Osteoporotic vertebral fractures (OVFs) are often not reported by radiologists on routine chest radiographs. This study aims to investigate the clinical value of a newly developed artificial intelligence (AI) tool, Ofeye 1.0, for automated detection of OVFs on lateral chest radiographs in post-menopausal women (>60 years) who were referred to undergo chest x-rays for other reasons. A total of 510 de-identified lateral chest radiographs from three clinical sites were retrieved and analysed using the Ofeye 1.0 tool. These images were then reviewed by a consultant radiologist with findings serving as the reference standard for determining the diagnostic performance of the AI tool for the detection of OVFs. Of all the original radiologist reports, missed OVFs were found in 28.8% of images but were detected using the AI tool. The AI tool demonstrated high specificity of 92.8% (95% CI: 89.6, 95.2%), moderate accuracy of 80.3% (95% CI: 76.3, 80.4%), positive predictive value (PPV) of 73.7% (95% CI: 65.2, 80.8%), and negative predictive value (NPV) of 81.5% (95% CI: 79, 83.8%), but low sensitivity of 49% (95% CI: 40.7, 57.3%). The AI tool showed improved sensitivity compared with the original radiologist reports, which was 20.8% (95% CI: 14.5, 28.4). The new AI tool can be used as a complementary tool in routine diagnostic reports for the reduction in missed OVFs in elderly women.
Collapse
Affiliation(s)
- Jenna Silberstein
- Discipline of Medical Radiation Science, Curtin Medical School, Curtin University, Perth, WA 6102, Australia;
| | - Cleo Wee
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (C.W.); (A.G.)
| | - Ashu Gupta
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (C.W.); (A.G.)
- Radiology Department, Fiona Stanley Hospital, Murdoch, WA 6105, Australia
| | - Hannah Seymour
- Department of Geriatrics and Aged Care, Fiona Stanley Hospital, Murdoch, WA 6150, Australia;
| | - Switinder Singh Ghotra
- Department of Radiology, Hospital of Yverdon-les-Bains (eHnv), 1400 Yverdon-les-Bains, Switzerland;
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), 1011 Lausanne, Switzerland;
| | - Cláudia Sá dos Reis
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), 1011 Lausanne, Switzerland;
| | - Guicheng Zhang
- School of Population Health, Curtin University, Perth, WA 6102, Australia;
| | - Zhonghua Sun
- Discipline of Medical Radiation Science, Curtin Medical School, Curtin University, Perth, WA 6102, Australia;
- Curtin Health Research Innovation Institute (CHIRI), Curtin University, Perth, WA 6102, Australia
| |
Collapse
|