Prediction of functional prognosis of orthopedic diseases using AI (Radiomics)
Project/Area Number |
20K18052
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Research Category |
Grant-in-Aid for Early-Career Scientists
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Allocation Type | Multi-year Fund |
Review Section |
Basic Section 56020:Orthopedics-related
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Research Institution | Chiba University |
Principal Investigator |
Maki Satoshi 千葉大学, 医学部附属病院, 助教 (00771982)
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Project Period (FY) |
2020-04-01 – 2023-03-31
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Project Status |
Completed (Fiscal Year 2022)
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Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2021: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2020: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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Keywords | 人工知能 / 脊髄損傷 / 頚椎症性脊髄症 / 大腿骨近位部骨折 / 転移性脊椎腫瘍 |
Outline of Research at the Start |
①脊髄損傷、②頚部脊髄症(頚髄の圧迫で手足が不自由になる病気)、③大腿骨近位部骨折、④転移性脊椎腫瘍(癌の背骨への転移)は高齢化社会において増加の一途を辿っている。これらの疾患の予後を知ることは適切な治療を行い、リハビリの目標を設定するのに非常に重要である。しかし、予後予測は複数の因子が関わっており、従来の画像評価法では予想が困難である。 Radiomicsとは人工知能(深層学習)を用いて医療用画像の特徴量を計測する事で臨床医には識別困難な遺伝的因子や臨床転機を予測する事である。本研究ではradiomicsを用いることによって①-④の項目について予測を行う
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Outline of Final Research Achievements |
Using MRI images, it has been found that the severity of paralysis in spinal cord injury patients can be predicted with 88% accuracy after one month, demonstrating the effectiveness of AI in this area. It has also been successful in predicting surgical outcomes for patients with cervical ossification of the posterior longitudinal ligament using machine learning based on patient information prior to surgery. Furthermore, by evaluating the ability of AI to detect femoral fractures using hip X-ray images, it has been found that there is potential to improve the accuracy of diagnosis. In addition, AI has been shown to be useful in identifying whether fractures are due to cancerous spinal metastasis or osteoporosis using MRI images. As a result, physicians can better understand diseases and patient care is expected to improve.
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Academic Significance and Societal Importance of the Research Achievements |
この研究は、医療の現場でAI技術を活用して、より正確な診断や治療法の選択を可能にすることで、患者のケアが向上することが期待される。また、医療従事者の負担を軽減し、効率的な医療サービスの提供に貢献することができる可能性がある。この研究は、医療の現場でAI技術を活用して、より正確な診断や治療法の選択を可能にすることで、患者のケアが向上することが期待されている。また、医療従事者の負担を軽減し、効率的な医療サービスの提供に貢献する可能性がある。
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Report
(4 results)
Research Products
(69 results)
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[Journal Article] Deep learning-based prediction model for postoperative complications?of cervical posterior longitudinal ligament ossification2023
Author(s)
Ito S, Nakashima H, Yoshii T, Egawa S, Sakai K, Kusano K, Tsutui S, Hirai T, Matsukura Y, Wada K, Katsumi K, Koda M, Kimura A, Furuya T, Maki S, Nagoshi N, Nishida N, Nagamoto Y, Oshima Y, Ando K, et al.
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Journal Title
European Spine Journal
Volume: -
Issue: 11
Pages: 3797-3806
DOI
Related Report
Peer Reviewed
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[Journal Article] Differentiating Magnetic Resonance Images of Pyogenic Spondylitis and Spinal Modic Change Using a Convolutional Neural Network2023
Author(s)
Mukaihata T, Maki S, Eguchi Y, Geundong K, Shoda J, Yokota H, Orita S, Shiga Y, Inage K, Furuya T, Ohtori S.
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Journal Title
Spine (Phila Pa 1976)
Volume: 48(4)
Issue: 4
Pages: 288-294
DOI
Related Report
Peer Reviewed
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[Journal Article] Magnetic resonance image segmentation of the compressed spinal cord in patients with degenerative cervical myelopathy using convolutional neural networks2023
Author(s)
Nozawa K, Maki S, Furuya T, Okimatsu S, Inoue T, Yunde A, Miura M, Shiratani Y, Shiga Y, Inage K, Eguchi Y, Ohtori S, Orita S.
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Journal Title
Int J Comput Assist Radiol Surg
Volume: 18(1)
Issue: 1
Pages: 45-54
DOI
Related Report
Peer Reviewed / Open Access
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[Journal Article] Efficacy of a machine learning-based approach in predicting neurological prognosis of cervical spinal cord injury patients following urgent surgery within 24 h after injury2022
Author(s)
Shimizu T, Suda K, Maki S, Koda M, Matsumoto Harmon S, Komatsu M, Ota M, Ushirozako H, Minami A, Takahata M, Iwasaki N, Takahashi H, Yamazaki M.
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Journal Title
J Clin Neurosci.
Volume: 107
Pages: 150-156
DOI
Related Report
Peer Reviewed
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[Journal Article] Automated fracture screening using an object detection algorithm on whole-body trauma computed tomography2022
Author(s)
Inoue T, Maki S, Furuya T, Mikami Y, Mizutani M, Takada I, Okimatsu S, Yunde A, Miura M, Shiratani Y, Nagashima Y, Maruyama J, Shiga Y, Inage K, Orita S, Eguchi Y, Ohtori S.
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Journal Title
Sci Rep
Volume: 12(1)
Issue: 1
Pages: 16549-16549
DOI
Related Report
Peer Reviewed
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[Journal Article] Determining the short-term neurological prognosis for acute cervical spinal cord injury using machine learning2022
Author(s)
Okimatsu S, Maki S, Furuya T, Fujiyoshi T, Kitamura M, Inada T, Aramomi M, Yamauchi T, Miyamoto T, Inoue T, Yunde A, Miura M, Shiga Y, Inage K, Orita S, Eguchi Y, Ohtori S.
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Journal Title
J Clin Neurosci
Volume: 96
Pages: 74-79
DOI
Related Report
Peer Reviewed
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[Journal Article] Detecting Distal Radial Fractures from Wrist Radiographs Using a Deep Convolutional Neural Network with an Accuracy Comparable to Hand Orthopedic Surgeons2022
Author(s)
Suzuki T, Maki S, Yamazaki T, Wakita H, Toguchi Y, Horii M, Yamauchi T, Kawamura K, Aramomi M, Sugiyama H, Matsuura Y, Yamashita T, Orita S, Ohtori S.
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Journal Title
J Digit Imaging
Volume: 35(1)
Issue: 1
Pages: 39-46
DOI
Related Report
Peer Reviewed
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[Journal Article] A Deep Convolutional Neural Network Using MRI2021
Author(s)
Yoda Takafumi、Maki Satoshi、Furuya Takeo、Yokota Hajime、Matsumoto Koji、Takaoka Hiromitsu、Miyamoto Takuya、Okimatsu Sho、Shiga Yasuhiro、Inage Kazuhide、Orita Sumihisa、Eguchi Yawara、Yamashita Takeshi、Masuda Yoshitada、Uno Takashi、Ohtori Seiji
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Journal Title
Spine
Volume: -
Issue: 8
Pages: E347-E352
DOI
Related Report
Peer Reviewed / Int'l Joint Research
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[Presentation] Machine learning approach in predicting clinically significant improvements after surgery in patients with cervical ossification of the posterior longitudinal ligament2022
Author(s)
Satoshi Maki, Toshitaka Yoshii, Takeo Furuya, Satoru Egawa, Kenichiro Sakai, Takashi Hirai, Keiichi Katsumi, Atsushi Kimura, Shiro Imagama, Masao Koda, Katsushi Takeshita, Morio Matsumoto, Masashi Yamazaki, Atsushi Okawa
Organizer
12th Annual Meeting and 2022 Instructuinal Course of Cervical Spine Research Society Asia Pacific Section
Related Report
Int'l Joint Research
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[Presentation] Diagnosis and prognostication of spinal pathologies using artificial intelligence.2022
Author(s)
Satoshi Maki, Takeo Furuya, Takaki Inoue, Atsushi Yunde, Masataka Miura, Yuki Shiratani, Yuki Nagashima, Juntaro Maruyama, Yasuhiro Shiga, Kazuhide Inage, Yawara Eguchi, Sumihisa Orita, Seiji Ohtori
Organizer
2022 Combined Meeting of SMISS-AP and International MISt Meeting
Related Report
Int'l Joint Research / Invited
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