Addition of 3D-CT evaluation to radiographic images and effect on diagnostic reliability of current 2018 AO/OTA classification of femoral trochanteric fractures

Purpose: Classification of femoral trochanteric fractures is usually based on plain X-ray findings using the Evans, Jensen, or AO/OTA classification. However, complications such as nonunion and cut out of the lag screw or blade are seen even in stable fracture. This may be due to the difficulty of exact diagnosis of fracture pattern in plain X-ray. Computed tomography (CT) may provide more information about the fracture pattern, but such data are scarce. In the present study, it was performed to propose a classification system for femoral trochanteric fractures using three-dimensional CT (3D-CT) and investigate the relationship between this classification and conventional plain X-ray classification. Methods: Using three-dimensional (3D)-CT, fractures were classified as two, three, or four parts using combinations of the head, greater trochanter, lesser trochanter, and shaft. We identified five subgroups of three-part fractures according to the fracture pattern involving the greater and lesser trochanters. In total, 239 femoral trochanteric fractures (45 men, 194 women; average age, 84.4 years) treated in four hospitals were classified using our 3D-CT classification. The relationship between this 3D-CT classification and the AO/OTA, Evans, and Jensen X-ray classifications was investigated. Results: In the 3D-CT classification, many fractures exhibited a large oblique fragment of the greater trochanter including the lesser trochanter. This fracture type was recognized as unstable in the 3D-CT classification but was often classified as stable in each X-ray classification. Conclusions: It is difficult to evaluate fracture patterns involving the greater trochanter, especially large oblique fragments including the lesser trochanter, using plain X-rays. The 3D-CT shows the fracture line very clearly, making it easy to classify the fracture pattern.

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Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals

Diagnostics ◽

10.3390/diagnostics11020233 ◽

2021 ◽

Vol 11 (2) ◽

pp. 233

Author(s):

Dong-Woon Lee ◽

Sung-Yong Kim ◽

Seong-Nyum Jeong ◽

Jae-Hong Lee

Keyword(s):

Dental Implant ◽

Classification Accuracy ◽

Reliability And Validity ◽

Mechanical Complication ◽

Radiographic Images ◽

Acceptable Accuracy ◽

Reliable Detection ◽

Classification Evaluation ◽

Accuracy Performance

Fracture of a dental implant (DI) is a rare mechanical complication that is a critical cause of DI failure and explantation. The purpose of this study was to evaluate the reliability and validity of a three different deep convolutional neural network (DCNN) architectures (VGGNet-19, GoogLeNet Inception-v3, and automated DCNN) for the detection and classification of fractured DI using panoramic and periapical radiographic images. A total of 21,398 DIs were reviewed at two dental hospitals, and 251 intact and 194 fractured DI radiographic images were identified and included as the dataset in this study. All three DCNN architectures achieved a fractured DI detection and classification accuracy of over 0.80 AUC. In particular, automated DCNN architecture using periapical images showed the highest and most reliable detection (AUC = 0.984, 95% CI = 0.900–1.000) and classification (AUC = 0.869, 95% CI = 0.778–0.929) accuracy performance compared to fine-tuned and pre-trained VGGNet-19 and GoogLeNet Inception-v3 architectures. The three DCNN architectures showed acceptable accuracy in the detection and classification of fractured DIs, with the best accuracy performance achieved by the automated DCNN architecture using only periapical images.

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Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

Biomolecules ◽

10.3390/biom11060815 ◽

2021 ◽

Vol 11 (6) ◽

pp. 815

Author(s):

Shintaro Sukegawa ◽

Kazumasa Yoshii ◽

Takeshi Hara ◽

Tamamo Matsuyama ◽

Katsusuke Yamashita ◽

...

Keyword(s):

Deep Learning ◽

Dental Implant ◽

Area Under The Curve ◽

Treatment Phase ◽

Panoramic Radiograph ◽

Radiographic Images ◽

Treatment Stage ◽

Prefectural Central Hospital ◽

Dental Panoramic Radiograph

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.

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The Usefulness of 3D-CT Evaluation for Gastric Cancer

Nihon Gekakei Rengo Gakkaishi (Journal of Japanese College of Surgeons) ◽

10.4030/jjcs.35.689 ◽

2010 ◽

Vol 35 (5) ◽

pp. 689-693

Author(s):

Yukio Nakamura ◽

Takeyoshi Yumiba ◽

Yusuke Watanabe ◽

Yoshio Yamasaki ◽

Yoshikazu Morimoto ◽

...

Keyword(s):

Gastric Cancer ◽

3D Ct ◽

Ct Evaluation

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Fracture morphology of AO/OTA 31-A trochanteric fractures: A 3D CT study with an emphasis on coronal fragments

Injury ◽

10.1016/j.injury.2016.12.015 ◽

2017 ◽

Vol 48 (2) ◽

pp. 277-284 ◽

Cited By ~ 14

Author(s):

Jae-Woo Cho ◽

William T. Kent ◽

Yong-Cheol Yoon ◽

Youngwoo Kim ◽

Hyungon Kim ◽

...

Keyword(s):

Fracture Morphology ◽

3D Ct ◽

Trochanteric Fractures

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The Anxiety-Depression Spectrum

The Oxford Handbook of Clinical Psychology ◽

10.1093/oxfordhb/9780199328710.013.033 ◽

2014 ◽

pp. 280-295

Author(s):

Ovsanna Leyfer ◽

Timothy A. Brown

Keyword(s):

Mood Disorders ◽

Emotional Disorders ◽

Discriminant Validity ◽

Diagnostic Reliability ◽

Continuous Increase ◽

Statistical Manual ◽

Diagnostic And Statistical Manual ◽

Anxiety Depression ◽

Review Current

The Diagnostic and Statistical Manual of Mental Disorders (DSM) has undergone considerable revisions since its first publication, with a continuous increase in the number of the anxiety and mood disorder categories. However, many researchers have expressed concern that the expansion of our nosology has resulted in less consideration of the overlapping features of emotional disorders. The purpose of this chapter is to review current issues and empirical evidence pertinent to the classification of anxiety and mood disorders and the relevance of these issues to treatment planning. It discusses discriminant validity, including diagnostic reliability and comorbidity, reviews the existing hierarchical models of emotional disorders, proposes a dimensional approach for classification of anxiety and mood disorders, and reviews transdiagnostic treatments of emotional disorders.

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