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Okino T, Ou Y, Ikebe M, Tamura K, Sutherland K, Fukae J, Tanimura K, Kamishima T. Fully automatic software for detecting radiographic joint space narrowing progression in rheumatoid arthritis: phantom study and comparison with visual assessment. Jpn J Radiol 2022; 41:510-520. [PMID: 36538163 DOI: 10.1007/s11604-022-01373-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE We have developed an in-house software equipped with partial image phase-only correlation (PIPOC) which can automatically quantify radiographic joint space narrowing (JSN) progression. The purpose of this study was to evaluate the software in phantom and clinical assessments. MATERIALS AND METHODS In the phantom assessment, the software's performance on radiographic images was compared to the joint space width (JSW) difference using a micrometer as ground truth. A phantom simulating a finger joint was scanned underwater. In the clinical assessment, 15 RA patients were included. The software measured the radiological progression of the finger joints between baseline and the 52nd week. The cases were also evaluated with the Genant-modified Sharp score (GSS), a conventional visual scoring method. We also quantitatively assessed these joints' synovial vascularity (SV) on power Doppler ultrasonography (0, 8, 20 and 52 weeks). RESULTS In the phantom assessment, the PIPOC software could detect changes in JSN with a smallest detectable difference of 0.044 mm at 0.1 mm intervals. In the clinical assessment, the JSW change of the joints with GSS progression detected by the software was significantly greater than those without GSS progression (p = 0.004). The JSW change of joints with positive SV at baseline was significantly higher than those with negative SV (p = 0.024). CONCLUSION Our in-house software equipped with PIPOC can automatically and quantitatively detect slight radiographic changes of JSW in clinically inactive RA patients.
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Validation of Fully Automatic Quantitative Software for Finger Joint Space Narrowing Progression for Rheumatoid Arthritis Patients. J Digit Imaging 2020; 33:1387-1392. [PMID: 32989619 DOI: 10.1007/s10278-020-00390-6] [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: 03/24/2020] [Revised: 08/08/2020] [Accepted: 09/14/2020] [Indexed: 10/23/2022] Open
Abstract
In rheumatoid arthritis (RA), the radiographic progression of joint space narrowing (JSN) is evaluated using visual assessments. However, those methods are complicated and time-consuming. We developed an automatic system that can detect joint locations and compute the joint space difference index (JSDI), which was defined as the chronological change in JSN between two radiographs. The purpose of this study was to establish the validity of the software that automatically evaluates the temporal change of JSN. This study consisted of 39 patients with RA. All patients were treated with tocilizumab and underwent hand radiography (left and right hand separately) at 0, 6, and 12 months. The JSN was evaluated using mTSS (modified Total Sharp Score) by one musculoskeletal radiologist as well as our automatic system. Software measurement showed that JSDI between 0 and 12 months was significantly higher than that between 0 and 6 months (p < 0.01). While, there was no significant difference in mTSS between 0, 6, and 12 months. The group with higher disease activity at 0 months had significantly higher JSDI between 0 and 6 months than that with lower disease activity (p = 0.02). The automatic software can evaluate JSN progression of RA patients in the finger joint on X-ray.
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Kato K, Sutherland K, Tanaka Y, Kato M, Fukae J, Tanimura K, Kamishima T. Fully automatic quantitative software for assessment of minute finger joint space narrowing progression on radiographs: evaluation in rheumatoid arthritis patients with long-term sustained clinical low disease activity. Jpn J Radiol 2020; 38:979-986. [PMID: 32488501 DOI: 10.1007/s11604-020-00996-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Accepted: 05/28/2020] [Indexed: 12/25/2022]
Abstract
PURPOSE Rheumatoid arthritis (RA) causes joint space narrowing (JSN) as a form of joint destruction. We developed an automatic system that can detect joint locations and compute the joint space difference index (JSDI), which was defined as the chronological change in JSN between two radiographs. This study aims to evaluate the application of "machine vision" for radiographic image of the finger joints. MATERIALS AND METHODS Fifteen RA patients with long-term sustained clinical low disease activity were recruited. All patients underwent hand radiography and power Doppler ultrasonography (PDUS). The JSN was evaluated using the Genant-modified Sharp scoring (GSS) method and the automatic system. Synovial vascularity (SV) was assessed quantitatively using ultrasonography. RESULTS There were no significant differences in the JSDI between the joints with JSN and those without JSN on GSS (p = 0.052). The JSDI of the joints with SV was significantly higher than those without SV (p = 0.043). The JSDI of the no therapeutic response group was significantly higher than those of the response group (p < 0.001). CONCLUSION Our software can automatically evaluate temporal changes of JSN, which might free rheumatologists / radiologists from the burden of scoring hand radiography.
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Affiliation(s)
- Kazuki Kato
- Radiation Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Japan
| | - Kenneth Sutherland
- Global Center for Biomedical Science and Engineering, Hokkaido University, North 15 West 7, Kita-ku, Sapporo, 060-8638, Japan
| | - Yuki Tanaka
- Graduate School of Health Sciences, Hokkaido University, North 12 West 5, Kita-ku, Sapporo, 060-0812, Japan
| | - Masaru Kato
- Division of Rheumatology, Endocrinology and Nephrology, Hokkaido University Graduate School of Medicine, N15 W7, Kita-ku, Sapporo, 060-8638, Japan
| | - Jun Fukae
- Department of Rheumatology, Hokkaido Medical Center for Rheumatic Diseases, Kotoni 1-3, Nishi-ku, Sapporo, 063-0811, Japan
| | - Kazuhide Tanimura
- Department of Rheumatology, Hokkaido Medical Center for Rheumatic Diseases, Kotoni 1-3, Nishi-ku, Sapporo, 063-0811, Japan
| | - Tamotsu Kamishima
- Faculty of Health Sciences, Hokkaido University, North-12 West-5, Kita-ku, Sapporo, 060-0812, Japan.
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Fujiwara K, Fang W, Okino T, Sutherland K, Furusaki A, Sagawa A, Kamishima T. Quick and accurate selection of hand images among radiographs from various body parts using deep learning. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:1199-1206. [PMID: 32925161 DOI: 10.3233/xst-200694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
BACKGROUND Although rheumatoid arthritis (RA) causes destruction of articular cartilage, early treatment significantly improves symptoms and delays progression. It is important to detect subtle damage for an early diagnosis. Recent software programs are comparable with the conventional human scoring method regarding detectability of the radiographic progression of RA. Thus, automatic and accurate selection of relevant images (e.g. hand images) among radiographic images of various body parts is necessary for serial analysis on a large scale. OBJECTIVE In this study we examined whether deep learning can select target images from a large number of stored images retrieved from a picture archiving and communication system (PACS) including miscellaneous body parts of patients. METHODS We selected 1,047 X-ray images including various body parts and divided them into two groups: 841 images for training and 206 images for testing. The training images were augmented and used to train a convolutional neural network (CNN) consisting of 4 convolution layers, 2 pooling layers and 2 fully connected layers. After training, we created software to classify the test images and examined the accuracy. RESULTS The image extraction accuracy was 0.952 and 0.979 for unilateral hand and both hands, respectively. In addition, all 206 test images were perfectly classified into unilateral hand, both hands, and the others. CONCLUSIONS Deep learning showed promise to enable efficiently automatic selection of target X-ray images of RA patients.
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Affiliation(s)
- Kohei Fujiwara
- Department of Health Sciences, Hokkaido University, Kita-ku, Sapporo, Japan
| | - Wanxuan Fang
- Faculty of Health Sciences, Hokkaido University, Kita-ku, Sapporo, Japan
| | - Taichi Okino
- Graduate School of Health Sciences, Hokkaido University, Kita-ku, Sapporo, Japan
| | - Kenneth Sutherland
- Global Center for Biomedical Science and Engineering, Hokkaido University, Kita-ku, Sapporo, Japan
| | - Akira Furusaki
- Sagawa Akira Rheumatology Clinic, Chuo-ku, Sapporo, Japan
| | - Akira Sagawa
- Sagawa Akira Rheumatology Clinic, Chuo-ku, Sapporo, Japan
| | - Tamotsu Kamishima
- Faculty of Health Sciences, Hokkaido University, Kita-ku, Sapporo, Japan
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Kato K, Yasojima N, Tamura K, Ichikawa S, Sutherland K, Kato M, Fukae J, Tanimura K, Tanaka Y, Okino T, Lu Y, Kamishima T. Detection of Fine Radiographic Progression in Finger Joint Space Narrowing Beyond Human Eyes: Phantom Experiment and Clinical Study with Rheumatoid Arthritis Patients. Sci Rep 2019; 9:8526. [PMID: 31189913 PMCID: PMC6561904 DOI: 10.1038/s41598-019-44747-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 05/24/2019] [Indexed: 12/12/2022] Open
Abstract
The visual assessment of joint space narrowing (JSN) on radiographs of rheumatoid arthritis (RA) patients such as the Genant-modified Sharp score (GSS) is widely accepted but limited by its subjectivity and insufficient sensitivity. We developed a software application which can assess JSN quantitatively using a temporal subtraction technique for radiographs, in which the chronological change in JSN between two radiographs was defined as the joint space difference index (JSDI). The aim of this study is to prove the superiority of the software in terms of detecting fine radiographic progression in finger JSN over human observers. A micrometer measurement apparatus that can adjust arbitrary joint space width (JSW) in a phantom joint was developed to define true JSW. We compared the smallest detectable changes in JSW between the JSDI and visual assessment using phantom images. In a clinical study, 222 finger joints without interval score change on GSS in 15 RA patients were examined. We compared the JSDI between joints with and without synovial vascularity (SV) on power Doppler ultrasonography during the follow-up period. True JSW difference was correlated with JSDI for JSW differences ranging from 0.10 to 1.00 mm at increments of 0.10 mm (R2 = 0.986 and P < 0.001). Rheumatologists were difficult to detect JSW difference of 0.30 mm or less. The JSDI of finger joints with SV was significantly higher than those without SV (P = 0.030). The software can detect fine differences in JSW that are visually unrecognizable.
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Affiliation(s)
- Kazuki Kato
- Graduate School of Health Sciences, Hokkaido University, North 12 West 5, Kita-ku, Sapporo, 060-0812, Japan
| | - Nobutoshi Yasojima
- Department of Radiology, NTT Sapporo Medical Center, South 1 West 15, Chuo-ku, Sapporo, 060-0061, Japan
| | - Kenichi Tamura
- Department of Mechanical Engineering, College of Engineering, Nihon University, Tokusada Aza Nakagawara 1, Tamura-cho, Koriyama, 963-8642, Japan
| | - Shota Ichikawa
- Department of Radiological Technology, Kurashiki Central Hospital, Miwa 1, Kurashiki, 710-8602, Japan
| | - Kenneth Sutherland
- Division of Photonic Bioimaging, Faculty of Medicine Research Center for Cooperative Projects, Hokkaido University, North 15 West 7, Kita-ku, Sapporo, 060-8638, Japan
| | - Masaru Kato
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, North 15 West 7, Kita-ku, Sapporo, 060-8638, Japan
| | - Jun Fukae
- Department of Rheumatology, Hokkaido Medical Center for Rheumatic Diseases, Kotoni 1-3, Nishi-ku, Sapporo, 063-0811, Japan
| | - Kazuhide Tanimura
- Department of Rheumatology, Hokkaido Medical Center for Rheumatic Diseases, Kotoni 1-3, Nishi-ku, Sapporo, 063-0811, Japan
| | - Yuki Tanaka
- Graduate School of Health Sciences, Hokkaido University, North 12 West 5, Kita-ku, Sapporo, 060-0812, Japan
| | - Taichi Okino
- Department of Radiological Technology, Sapporo City General Hospital, North 11 West 13, Chuo-ku, Sapporo, 060-8604, Japan
| | - Yutong Lu
- Faculty of Health Sciences, Hokkaido University, North-12 West-5, Kita-ku, Sapporo, 060-0812, Japan
| | - Tamotsu Kamishima
- Faculty of Health Sciences, Hokkaido University, North-12 West-5, Kita-ku, Sapporo, 060-0812, Japan.
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