Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images

Yabu, Akito; Hoshino, Minoru; Tabuchi, Hitoshi; Takahashi, Shinji; Masumoto, Hiroki; Akada, Masahiro; Morita, Shoji; Maekawa, Takafumi; Iwamae, Masayoshi; Inose, Hiroyuki; Kato, Tsuyoshi; Yoshii, Toshitaka; Tsujio, Tadao; Terai, Hidetomi; Toyoda, Hiromitsu; Suzuki, Akinobu; Tamai, Katsuto; Ohyama, Shoichiro; Hori, Yusuke; Okawa, Atsushi; Nakamura, Hiroaki

doi:10.1016/j.spinee.2021.03.006

Cited by 44 publications

(23 citation statements)

References 21 publications

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“…By contrast, Yabu et al presented a CNN model using MRI images as the training data. This model showed a higher accuracy (88%) than that of the surgeons [25].…”

Section: Deep Learning For Fracturesmentioning

confidence: 75%

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Lee

Chung

2022

Applied Sciences

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Since its development, deep learning has been quickly incorporated into the field of medicine and has had a profound impact. Since 2017, many studies applying deep learning-based diagnostics in the field of orthopedics have demonstrated outstanding performance. However, most published papers have focused on disease detection or classification, leaving some unsatisfactory reports in areas such as segmentation and prediction. This review introduces research published in the field of orthopedics classified according to disease from the perspective of orthopedic surgeons, and areas of future research are discussed. This paper provides orthopedic surgeons with an overall understanding of artificial intelligence-based image analysis and the information that medical data should be treated with low prejudice, providing developers and researchers with insight into the real-world context in which clinicians are embracing medical artificial intelligence.

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“…By contrast, Yabu et al presented a CNN model using MRI images as the training data. This model showed a higher accuracy (88%) than that of the surgeons [25].…”

Section: Deep Learning For Fracturesmentioning

confidence: 75%

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Lee

Chung

2022

Applied Sciences

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“…[31][32][33] For example, Yeh et al 30 showed that an AI-baed automatic alignment measure system can locate spinal anatomic landmarks with a high accuracy and produce radiographic parameters that correlated well with operator-based measurements. Yabu et al 34 also demonstrated that convolutional neural network could detect new osteoporotic vertebral fractures utilising magnetic resonance images with performance comparable to that of spine surgeons. Moreover, Karnuta et al 18 used AI to differentiate between knee arthroplasty implants from different manufacturers with near-perfect accuracy.…”

Section: Discussionmentioning

confidence: 99%

Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty

Zhang

Ding

et al. 2021

Robotics Computer Surgery

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Background: Robotic-assisted total knee arthroplasty (TKA) was performed to promote the accuracy of bone resection and mechanical alignment. Among these TKA system procedures, 3D reconstruction of CT data of lower limbs consumes significant manpower. Artificial intelligence (AI) algorithms applying deep learning has been proved efficient in automated identification and visual processing.Methods: CT data of a total of 200 lower limbs scanning were used for AI-based 3D model construction and CT data of 20 lower limbs scanning were utilised for verification. Results:We showed that the performance of an AI-guided 3D reconstruction of CT data of lower limbs for robotic-assisted TKA was similar to that of the operatorbased approach. The time of 3D lower limb model construction using AI was 4.7 min. AI-based 3D models can be used for surgical planning. Conclusion:AI was used for the first time to guide the 3D reconstruction of CT data of lower limbs for facilitating robotic-assisted TKA. Incorporation of AI in 3D model reconstruction before TKA might reduce the workload of radiologists. K E Y W O R D Sartificial intelligence, HURWA robotic-assisted TKA system, robotically TKA assisted system, 3D CT model | INTRODUCTIONTotal knee arthroplasty (TKA) is an effective procedure for cases with end-stage rheumatoid arthritis or osteoarthritis of the knee. 1 The surgery was carried out in over 370,000 and 700,000 patients annually in China and USA, respectively. 2 Despite the development in implant materials and design, thromboembolic prophylaxis, rehabilitation, computer navigation and patient-specific implants, and the use of prophylactic antibiotics, over 20% of patients reported unsatisfaction following TKA. [3][4][5][6] Robotically assisted TKA was thus developed to promote mechanical alignment, accuracy of bone resection, soft tissue protection, and implant survivorship and implant stability. [7][8][9][10] In this connection, our previous studies showed that a novel HURWA robotic-assisted TKA system (BEIJING HURWA-ROBOT Technology Co. Ltd) could enhance the accuracy of bone resection in terms of resection angles and levels in the sawbone and the sheep models. 11,12 The procedures of robotic-assisted TKA system consists of the following steps: preoperative computed tomography (CT) scanning of lower limbs, three-dimensional (3D) reconstruction of CT data of lower limbs, CT-based planning, surgical planning depending on the 3D CT model, and the act of performing robotic-assisted TKA. Among

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“…In recent a few years, a number of authors reported AI (artificial Intelligence) enabled analysis and detection of VF (vertebral fracture) of spine medical images which included spine radiograph ( 46 - 53 ), DXA ( 54 - 56 ), thoracic and/or abdominal CT ( 57 - 62 ), and spine MR images ( 63 - 66 ). Kim et al ( 46 ) presented a structured hierarchical segmentation method that combines the advantages of two deep-learning methods of pose-driven learning and M-net which allows automated detection and segmentation of lumbar vertebrae from radiograph for CVF evaluation.…”

Section: Discussionmentioning

confidence: 99%

“…Rueckel et al ( 61 ) described several pathology-specific AI algorithms enabled detection of relevant initially missed secondary thoracic findings in emergency whole-body CT scans, including the detection of cardiomegaly, coronary artery plaques, lung lesions, aortic aneurysms, and VFs. 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.…”

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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Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images

Cited by 44 publications

References 21 publications

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty

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

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