SAT0543 AUTOMATIC DETECTION OF HAND JOINT REGION, ANKYLOSIS AND SUBLUXATION IN RADIOGRAPHIC IMAGES USING DEEP LEARNING: DEVELOPMENT OF ARTIFICIAL INTELLIGENCE-BASED RADIOGRAPHIC EVALUATION SYSTEM FOR BONE DESTRUCTION. (June 2019)

• Record Type: Journal Article
• Authors: Izumi, Keisuke; Suzuki, Kanata; Hashimoto, Masahiro; Endoh, Toshio; Doi, Kentaro; Iwai, Yuki; Inamo, Jun; Ota, Yuichiro; Kajio, Nobuhiko; Kikuchi, Jun; Sakata, Komei; Takanashi, Satoshi; Takahashi, Chihiro; Takei, Hiroshi; Tamai, Hiroya; Hiramoto, Kazuoto; Kaneko, Yuko; Jinzaki, Masahiro; Ko, Shigeru; Takeuchi, Tsutomu
• Is Part Of: Annals of the rheumatic diseases. Volume 78(2019)Supplement 2
• Page Start: 1364
• Page End: 1364
• Publication Date: 2019-06
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2019-eular.4801
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Artificial intelligence (AI) techniques including deep learning have been rapidly evolving and have yielded appreciable benefits in many fields in recent years. In rheumatology field, however, these techniques have not been used often. Objectives: In an early phase of development of an AI-based automatic radiographic evaluating system for bone destruction, we aimed to develop learning-based models to automatically detect hand joint region, ankylosis and subluxation in radiographic images. Methods: A total of 130 radiographic image sets of both hands were randomly obtained from rheumatoid arthritis patients who had visited our division at Keio University Hospital in 2015. Well-trained rheumatologists determined the boundaries of regions of MP and PIP/IP joints and evaluated the presence of ankylosis and subluxation of each joint in radiographs. These evaluations of hand joints were performed using our developed annotation software tool [1]. In learning phase, joint images were randomly divided into five sets for 5-fold cross validation. As deep learning models, we utilized Single Shot Multibox Detector (SSD) method [2] with ensemble learning for detecting ankylosis and subluxation of MP and PIP/IP joint regions. Results: Our model showed 100% detection rate of MP and PIP/IP joint regions. As a performance of detecting hand joint ankylosis and subluxation, our model presented precision values of 0.85 and 0.73, recall values of 0.94 and 0.79, and F-measure values of 0.90 and 0.76, respectively. Conclusion: Deep learning-based models to automatically detect hand joint region, ankylosis and subluxation in radiographic images were developed with relatively small samples, which suggests that the predictive performance may increase by collecting more training dataset. Next, we are elaborating a plan for a deep learning-based evaluating system for erosion and joint space narrowing. References: [1] Izumi K, Hashimoto M, Suzuki K, Endoh T, Doi K, Iwai Y, Jinzaki M, Ko S, Takeuchi T. Detecting Hand Joint Ankylosis in Radiographic Images Using Deep Learning: A Step in Developing Automatic Radiographic Scoring System for Bone Destruction. Arthritis Rheumatol 2018;70(suppl 10). [2] Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC. SSD: single shot multibox detector. European Conference on Computer Vision (ECCV) 2016. Acknowledgement: Keisuke Izumi and Kanata Suzuki are contributed equally. Disclosure of Interests: Keisuke Izumi Grant/research support from: Asahi Kasei Pharma, Speakers bureau: Asahi Kasei Pharma Corp, Astellas Pharma Inc., Bristol Myers Squibb, Chugai Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Mitsubishi Tanabe Pharma Co., Kanata Suzuki Employee of: Fujitsu Laboratories Ltd., Masahiro Hashimoto: None declared, Toshio Endoh Employee of: Fujitsu Laboratories Ltd., Kentaro Doi Employee of: Fujitsu Ltd., Yuki Iwai Employee of: Fujitsu Ltd., Jun Inamo: None declared, Yuichiro Ota: None declared, Nobuhiko Kajio: None declared, Jun Kikuchi: None declared, Komei Sakata: None declared, Satoshi Takanashi: None declared, Chihiro Takahashi: None declared, Hiroshi Takei: None declared, Hiroya Tamai: None declared, Kazuoto Hiramoto: None declared, Yuko Kaneko Speakers bureau: AbbVie, Eisai Pharmaceutical, Chugai Pharmaceutical Co., Ltd., Bristol Myers Squibb, Astellas Pharma Inc., Mitsubishi Tanabe Pharma Co., Pfizer Japan Inc., Janssen Pharmaceutical K.K., Eli Lilly Japan K.K., Santen Pharmaceutical Co., Ltd., Kyowa Hakko Kirin Co. Ltd. and UCB Japan Co. Ltd., Masahiro Jinzaki: None declared, Shigeru Ko Grant/research support from: Fujitsu Ltd., Tsutomu Takeuchi Grant/research support from: Astellas Pharma Inc, Chugai Pharmaceutical Co, Ltd., Daiichi Sankyo Co., Ltd., Takeda Pharmaceutical Co., Ltd., AbbVie GK, Asahikasei Pharma Corp., Mitsubishi Tanabe Pharma Co., Pfizer Japan Inc., Eisai Co., Ltd., AYUMI Pharmaceutical Corporation, Nipponkayaku Co. Ltd., Novartis Pharma K.K., Grant/research support from: AbbVie, Asahi Kasei, Astellas, AstraZeneca, AYUMI, Bristol-Myers Squibb, Chugai, Daiichi Sankyo, Eisai, Eli Lilly Japan, Janssen, Mitsubishi Tanabe, Nippon Kayaku, Novartis, Pfizer Japan Inc, Taiho, Taisho Toyama, Takeda, Teijin, Grant/research support from: Astellas Pharma Inc., Bristol Myers Squibb, Chugai Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Co., Pfizer Japan Inc., Santen Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co., Ltd., Teijin Pharma Ltd., AbbVie GK, Asahi Kasei Pharma Corp., Taisho Toyama Pharmaceutical Co., Ltd., SymBio Pharmaceuticals Ltd., Janssen Pharmaceutical K.K., Celltrion Inc., Nipponkayaku Co. Ltd., and UCB Japan, Consultant for: Astra Zeneca K.K., Eli Lilly Japan K.K., Novartis Pharma K.K., Mitsubishi Tanabe Pharma Co., Abbivie GK, Nipponkayaku Co.Ltd, Janssen Pharmaceutical K.K., Astellas Pharma Inc., Taiho Pharmaceutical Co. Ltd., Chugai Pharmaceutical Co. Ltd., Taisho Toyama Pharmaceutical Co. Ltd., GlaxoSmithKline K.K., UCB Japan Co. Ltd., Consultant for: AbbVie, Asahi Kasei, Astellas, AstraZeneca, AYUMI, Bristol-Myers Squibb, Chugai, Daiichi Sankyo, Eisai, Eli Lilly Japan, Janssen, Mitsubishi Tanabe, Nippon Kayaku, Novartis, Pfizer Japan Inc, Taiho, Taisho Toyama, Takeda, Teijin, Consultant for: Astra Zeneca K.K., Eli Lilly Japan K.K., Novartis Pharma K.K., Mitsubishi Tanabe Pharma Co., Asahi Kasei Medical K.K., AbbVie GK, Daiichi Sankyo Co., Ltd., Bristol Myers Squibb, and Nipponkayaku Co. Ltd., Speakers bureau: Astellas Pharma Inc., Bristol Myers Squibb, Chugai Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Co., Pfizer Japan Inc., Santen Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co., Ltd., Teijin Pharma Ltd., AbbVie GK, Asahi Kasei Pharma Corp., Taisho Toyama Pharmaceutical Co., Ltd., SymBio Pharmaceuticals Ltd., Janssen Pharmaceutical K.K., Celltrion Inc., Nipponkayaku Co. Ltd., and UCB Japan, Speakers bureau: AbbVie, Asahi Kasei, Astellas, AstraZeneca, AYUMI, Bristol-Myers Squibb, Chugai, Daiichi Sankyo, Eisai, Eli Lilly Japan, Janssen, Mitsubishi Tanabe, Nippon Kayaku, Novartis, Pfizer Japan Inc, Taiho, Taisho Toyama, Takeda, Teijin, Speakers bureau: AbbVie GK., Bristol–Myers K.K., Chugai Pharmaceutical Co. Ltd., Mitsubishi Tanabe Pharma Co., Pfizer Japan Inc., Astellas Pharma Inc, Diaichi Sankyo Co. Ltd., Eisai Co. Ltd., Sanofi K.K., Teijin Pharma Ltd., Takeda Pharmaceutical Co. Ltd., Novartis Pharma K.K.