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Diagnostics
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Computer Aided Diagnosis in Orthopaedics
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Computer Aided Diagnosis in Orthopaedics

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 14334

Special Issue Editors

Dr. Yuichi Yoshii

E-Mail Website
Guest Editor
Department of Orthopaedic Surgery, Tokyo Medical University Ibaraki Medical Center, Ami, Ibaraki, Japan
Interests: orthopaedic surgery; computer-assisted surgery; computer vision; biomechanics; hand surgery
Special Issues, Collections and Topics in MDPI journals
Dr. Kunihiro Oka

E-Mail Website
Guest Editor
Department of Orthopaedic Surgery, Osaka University, Graduate School of Medicine, Suita, Osaka, Japan
Interests: computer-aided surgery; biomechanics; hand surgery; traumatology; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Chien-Kuo Wang

E-Mail Website
Guest Editor
Department of Medical Imaging, National Cheng Kung University Hospital, Tainan, Taiwan
Interests: musculoskeletal radiology; spine imaging; computer-aided diagnosis; interventional radiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer aided diagnosis is a method of image information analysis, using computer based information processing technology and actively utilizing the results for diagnosis. In recent years, the usefulness of computer aided diagnosis in various orthopaedic disorders has been reported. Three-dimensional bone morphology evaluation, preoperative planning, intraoperative navigation, and robotic surgery based on computer-assisted technology are considered to be effective means. It is becoming an important element in orthopaedics by improving the accuracy of diagnosis and surgery, reducing complications, and leading to better prognosis. In addition, artificial intelligence (AI) based medical care for orthopaedic diseases has also made remarkable progress.

The aims of this Special Issue of ‘Computer Aided Diagnosis in Orthopaedics’ are: to describe new and established diagnostic and treatment modalities for various orthopaedic disorders using computer assistance technologies; to discuss clinical approaches using computer aided diagnosis from frequently encountered conditions to intractable diseases; and to review new research developments in the field of computer aided diagnosis in orthopaedics. This issue will focus on the topics related to computer assisted evaluations, artificial intelligence, preoperative planning, bone morphological analysis, visualizations, and combined modalities.

Dr. Yuichi Yoshii
Dr. Kunihiro Oka
Dr. Chien-Kuo Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Computer aided diagnosis
  • Artificial intelligence
  • Computer vision
  • Computer assisted orthopaedic surgery
  • Computer surgical planning
  • Navigation surgery
  • Musculoskeletal radiology

Published Papers (5 papers)

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Research

13 pages, 3735 KiB  
Article
Scaphoid Fracture Detection by Using Convolutional Neural Network
by Tai-Hua Yang, Ming-Huwi Horng, Rong-Shiang Li and Yung-Nien Sun
Diagnostics 2022, 12(4), 895; https://doi.org/10.3390/diagnostics12040895 - 04 Apr 2022
Cited by 9 | Viewed by 5053
Abstract
Scaphoid fractures frequently appear in injury radiograph, but approximately 20% are occult. While there are few studies in the fracture detection of X-ray scaphoid images, their effectiveness is insignificant in detecting the scaphoid fractures. Traditional image processing technology had been applied to segment [...] Read more.
Scaphoid fractures frequently appear in injury radiograph, but approximately 20% are occult. While there are few studies in the fracture detection of X-ray scaphoid images, their effectiveness is insignificant in detecting the scaphoid fractures. Traditional image processing technology had been applied to segment interesting areas of X-ray images, but it always suffered from the requirements of manual intervention and a large amount of computational time. To date, the models of convolutional neural networks have been widely applied to medical image recognition; thus, this study proposed a two-stage convolutional neural network to detect scaphoid fractures. In the first stage, the scaphoid bone is separated from the X-ray image using the Faster R-CNN network. The second stage uses the ResNet model as the backbone for feature extraction, and uses the feature pyramid network and the convolutional block attention module to develop the detection and classification models for scaphoid fractures. Various metrics such as recall, precision, sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) are used to evaluate our proposed method’s performance. The scaphoid bone detection achieved an accuracy of 99.70%. The results of scaphoid fracture detection with the rotational bounding box revealed a recall of 0.789, precision of 0.894, accuracy of 0.853, sensitivity of 0.789, specificity of 0.90, and AUC of 0.920. The resulting scaphoid fracture classification had the following performances: recall of 0.735, precision of 0.898, accuracy of 0.829, sensitivity of 0.735, specificity of 0.920, and AUC of 0.917. According to the experimental results, we found that the proposed method can provide effective references for measuring scaphoid fractures. It has a high potential to consider the solution of detection of scaphoid fractures. In the future, the integration of images of the anterior–posterior and lateral views of each participant to develop more powerful convolutional neural networks for fracture detection by X-ray radiograph is probably important to research. Full article
(This article belongs to the Special Issue Computer Aided Diagnosis in Orthopaedics)
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9 pages, 4282 KiB  
Article
BatchBMD as an Efficient and Accurate Dual-Energy X-ray Absorptiometry Report Generator
by Chun-Hsiang Chan, Wen-Chi Huang, Yi-Chien Lu, Hsing-Fen Hsiao and Wing P. Chan
Diagnostics 2021, 11(12), 2403; https://doi.org/10.3390/diagnostics11122403 - 20 Dec 2021
Viewed by 2224
Abstract
Dual-energy X-ray absorptiometry is the gold standard for evaluating Bone Mineral Density (BMD); however, a typical BMD report is generated in a time-inefficient manner and is prone to error. We developed a rule-based automated reporting system, BatchBMD, that accelerates DXA reporting while improving [...] Read more.
Dual-energy X-ray absorptiometry is the gold standard for evaluating Bone Mineral Density (BMD); however, a typical BMD report is generated in a time-inefficient manner and is prone to error. We developed a rule-based automated reporting system, BatchBMD, that accelerates DXA reporting while improving its accuracy over current systems. BatchBMD generates a structured report, customized to the specific clinical purpose. To compare BatchBMD to a Web-based Reporting (WBR) system for efficiency and accuracy, 500 examinations were randomly chosen from those performed at the Taipei Municipal Wanfang Hospital from January to March 2021. The final assessment included all 2326 examinations conducted from September 2020 to March 2021. The average reporting times were 6.7 and 10.8 min for BatchBMD and the WBR system, respectively, while accuracy was 99.4% and 98.2%, respectively. Most of the errors made by BatchBMD were digit errors in the appendicular skeletal muscle index. After correcting this, 100% accuracy across all 2326 examinations was validated. This automated and accurate BMD reporting system significantly reduces report production workload for radiologists and technicians while increasing productivity and quality. Additionally, the portable software, which employs a simple framework, can reduce deployment costs in clinical practice. Full article
(This article belongs to the Special Issue Computer Aided Diagnosis in Orthopaedics)
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16 pages, 4630 KiB  
Article
Preoperative Evaluation and Surgical Simulation for Osteochondritis Dissecans of the Elbow Using Three-Dimensional MRI-CT Image Fusion Images
by Sho Kohyama, Yasumasa Nishiura, Yuki Hara, Takeshi Ogawa, Akira Ikumi, Eriko Okano, Yasukazu Totoki, Yuichi Yoshii and Masashi Yamazaki
Diagnostics 2021, 11(12), 2337; https://doi.org/10.3390/diagnostics11122337 - 11 Dec 2021
Cited by 5 | Viewed by 2385
Abstract
We used our novel three-dimensional magnetic resonance imaging-computed tomography fusion images (3D MRI-CT fusion images; MCFIs) for detailed preoperative lesion evaluation and surgical simulation in osteochondritis dissecans (OCD) of the elbow. Herein, we introduce our procedure and report the findings of the assessment [...] Read more.
We used our novel three-dimensional magnetic resonance imaging-computed tomography fusion images (3D MRI-CT fusion images; MCFIs) for detailed preoperative lesion evaluation and surgical simulation in osteochondritis dissecans (OCD) of the elbow. Herein, we introduce our procedure and report the findings of the assessment of its utility. We enrolled 16 men (mean age: 14.0 years) and performed preoperative MRI using 7 kg axial traction with a 3-Tesla imager and CT. Three-dimensional-MRI models of the humerus and articular cartilage and a 3D-CT model of the humerus were constructed. We created MCFIs using both models. We validated the findings obtained from the MCFIs and intraoperative findings using the following items: articular cartilage fissures and defects, articular surface deformities, vertical and horizontal lesion diameters, the International Cartilage Repair Society (ICRS) classification, and surgical procedures. The MCFIs accurately reproduced the lesions and correctly matched the ICRS classification in 93.5% of cases. Surgery was performed as simulated in all cases. Preoperatively measured lesion diameters exhibited no significant differences compared to the intraoperative measurements. MCFIs were useful in the evaluation of OCD lesions and detailed preoperative surgical simulation through accurate reproduction of 3D structural details of the lesions. Full article
(This article belongs to the Special Issue Computer Aided Diagnosis in Orthopaedics)
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10 pages, 1886 KiB  
Article
New Evaluation Method for Bone Formation around a Fully Hydroxyapatite-Coated Stem Using Digital Tomosynthesis: A Retrospective Cross-Sectional Study
by Sho Totsuka, Tomofumi Nishino, Ryunosuke Watanabe, Masashi Yamazaki and Hajime Mishima
Diagnostics 2021, 11(11), 2094; https://doi.org/10.3390/diagnostics11112094 - 12 Nov 2021
Viewed by 1542
Abstract
Digital tomosynthesis (DTS) is a new imaging technique derived from radiography, and its usefulness has been gradually reported in the field of orthopedic diagnosis in recent years. A fully hydroxyapatite (HA)-coated stem, which is used for total hip arthroplasty (THA), is a type [...] Read more.
Digital tomosynthesis (DTS) is a new imaging technique derived from radiography, and its usefulness has been gradually reported in the field of orthopedic diagnosis in recent years. A fully hydroxyapatite (HA)-coated stem, which is used for total hip arthroplasty (THA), is a type of cementless stem that has been widely used recently and reported to have good results. However, stem loosening on plain radiographs is difficult to determine in some cases due to cancellous condensation around the stem. In this retrospective cross-sectional study, we compared the results of plain radiography versus DTS to evaluate the imaging findings after THA using a fully HA-coated stem. Twenty joints each in the 3 y and 1 y postoperative groups underwent plain radiography and DTS. On DTS, bone formation around the stem was confirmed in all cases; however, this formation was not reproducible on plain radiography, and there were cases in which the reaction could not be confirmed or cases with cancellous condensation resembling reactive lines. This reaction was not reproducible on plain radiographs, and in some cases, the reaction could not be confirmed, or there were cases with cancellous condensation that resembled reactive lines. Therefore, DTS was useful in the diagnosis of bone formation around the implant. Full article
(This article belongs to the Special Issue Computer Aided Diagnosis in Orthopaedics)
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12 pages, 2531 KiB  
Article
Comparison of the Predicting Performance for Fate of Medial Meniscus Posterior Root Tear Based on Treatment Strategies: A Comparison between Logistic Regression, Gradient Boosting, and CNN Algorithms
by Jae-Ik Lee, Dong-Hyun Kim, Hyun-Jin Yoo, Han-Gyeol Choi and Yong-Seuk Lee
Diagnostics 2021, 11(7), 1225; https://doi.org/10.3390/diagnostics11071225 - 07 Jul 2021
Cited by 3 | Viewed by 1855
Abstract
This study aimed to validate the accuracy and prediction performance of machine learning (ML), deep learning (DL), and logistic regression methods in the treatment of medial meniscus posterior root tears (MMPRT). From July 2003 to May 2018, 640 patients diagnosed with MMPRT were [...] Read more.
This study aimed to validate the accuracy and prediction performance of machine learning (ML), deep learning (DL), and logistic regression methods in the treatment of medial meniscus posterior root tears (MMPRT). From July 2003 to May 2018, 640 patients diagnosed with MMPRT were included. First, the affecting factors for the surgery were evaluated using statistical analysis. Second, AI technology was introduced using X-ray and MRI. Finally, the accuracy and prediction performance were compared between ML&DL and logistic regression methods. Affecting factors of the logistic regression method corresponded well with the feature importance of the six top-ranked factors in the ML&DL method. There was no significant difference when comparing the accuracy, F1-score, and error rate between ML&DL and logistic regression methods (accuracy = 0.89 and 0.91, F1 score = 0.89 and 0.90, error rate = 0.11 and 0.09; p = 0.114, 0.422, and 0.119, respectively). The area under the curve (AUC) values showed excellent test quality for both ML&DL and logistic regression methods (AUC = 0.97 and 0.94, respectively) in the evaluation of prediction performance (p = 0.289). The affecting factors of the logistic regression method and the influence of the ML&DL method were not significantly different. The accuracy and performance of the ML&DL method in predicting the fate of MMPRT were comparable to those of the logistic regression method. Therefore, this ML&DL algorithm could potentially predict the outcome of the MMRPT in various fields and situations. Furthermore, our method could be efficiently implemented in current clinical practice. Full article
(This article belongs to the Special Issue Computer Aided Diagnosis in Orthopaedics)
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