Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Deep neural networks with promising diagnostic accuracy for the classification of atypical femoral fractures.

ABSTRACT: Background and purpose - A correct diagnosis is essential for the appropriate treatment of patients with atypical femoral fractures (AFFs). The diagnostic accuracy of radiographs with standard radiology reports is very poor. We derived a diagnostic algorithm that uses deep neural networks to enable clinicians to discriminate AFFs from normal femur fractures (NFFs) on conventional radiographs.Patients and methods - We entered 433 radiographs from 149 patients with complete AFF and 549 radiographs from 224 patients with NFF into a convolutional neural network (CNN) that acts as a core classifier in an automated pathway and a manual intervention pathway (manual improvement of image orientation). We tested several deep neural network structures (i.e., VGG19, InceptionV3, and ResNet) to identify the network with the highest diagnostic accuracy for distinguishing AFF from NFF. We applied a transfer learning technique and used 5-fold cross-validation and class activation mapping to evaluate the diagnostic accuracy.Results - In the automated pathway, ResNet50 had the highest diagnostic accuracy, with a mean of 91% (SD 1.3), as compared with 83% (SD 1.6) for VGG19, and 89% (SD 2.5) for InceptionV3. The corresponding accuracy levels for the intervention pathway were 94% (SD 2.0), 92% (2.7), and 93% (3.7), respectively. With regards to sensitivity and specificity, ResNet outperformed the other networks with a mean AUC (area under the curve) value of 0.94 (SD 0.01) and surpassed the accuracy of clinical diagnostics.Interpretation - Artificial intelligence systems show excellent diagnostic accuracies for the rare fracture type of AFF in an experimental setting.

SUBMITTER: Zdolsek G

PROVIDER: S-EPMC8381921 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs.

Project description:Background and purpose - Deep-learning approaches based on convolutional neural networks (CNNs) are gaining interest in the medical imaging field. We evaluated the diagnostic performance of a CNN to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using antero-posterior (AP) and lateral hip radiographs. Patients and methods - 1,703 plain hip AP radiographs and 1,220 plain hip lateral radiographs were included in the total dataset. 150 images each of the AP and lateral views were separated out and the remainder of the dataset was used for training. The CNN made the diagnosis based on: (1) AP radiographs alone, (2) lateral radiographs alone, or (3) both AP and lateral radiographs combined. The diagnostic performance of the CNN was measured by the accuracy, recall, precision, and F1 score. We further compared the CNN's performance with that of orthopedic surgeons. Results - The average accuracy, recall, precision, and F1 score of the CNN based on both anteroposterior and lateral radiographs were 0.98, 0.98, 0.98, and 0.98, respectively. The accuracy of the CNN was comparable to, or statistically significantly better than, that of the orthopedic surgeons regardless of radiographic view used. In the CNN model, the accuracy of the diagnosis based on both views was significantly better than the lateral view alone and tended to be better than the AP view alone. Interpretation - The CNN exhibited comparable or superior performance to that of orthopedic surgeons to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using both AP and lateral hip radiographs.

| S-EPMC8023868 | biostudies-literature

Atypical femoral fractures and biphosphonates.

Project description:[No Abstract Available].

| S-EPMC5556307 | biostudies-other

Dichotomous location of 160 atypical femoral fractures.

Project description:Background and purposeThe risk of atypical fracture of the femur is associated with bisphosphonate use. While characterizing atypical fractures from a previous nationwide study in radiographic detail, we had the impression that the fractures were located either in the subtrochanteric region or in the shaft. We determined whether there is a dichotomy in this respect.Patients and methodsThe distance between the atypical fractures and the lesser trochanter was measured on plain radiographs from 129 of 160 patients with atypical fractures, taken from 2008 through 2010. Analysis of the distances measured showed 2 clusters, which were then analyzed with regard to bisphosphonate use and age.ResultsThe distribution of the distances would be best described as 2 clusters, with a dichotomy at 8 cm. The proximal (subtrochanteric) cluster comprised 25 patients who were generally younger (median 71 years) than the 104 patients in the cluster with shaft fractures (median 80 years). The 95% CI for the difference between medians was 4-11 years. Of the patients with subtrochanteric fractures, 18 of 25 used bisphosphonates as compared to 89 of 104 with shaft fractures.InterpretationThe younger age and possibly smaller proportion of bisphosphonate users in the subtrochanteric cluster may be compatible with a greater influence of mechanical stress in the underlying pathophysiology of proximal fractures.

| S-EPMC3851670 | biostudies-literature

Classification of crystallization outcomes using deep convolutional neural networks.

Project description:The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.

| S-EPMC6010233 | biostudies-other

Deep neural networks for active wave breaking classification.

Project description:Wave breaking is an important process for energy dissipation in the open ocean and coastal seas. It drives beach morphodynamics, controls air-sea interactions, determines when ship and offshore structure operations can occur safely, and influences on the retrieval of ocean properties from satellites. Still, wave breaking lacks a proper physical understanding mainly due to scarce observational field data. Consequently, new methods and data are required to improve our current understanding of this process. In this paper we present a novel machine learning method to detect active wave breaking, that is, waves that are actively generating visible bubble entrainment in video imagery data. The present method is based on classical machine learning and deep learning techniques and is made freely available to the community alongside this publication. The results indicate that our best performing model had a balanced classification accuracy score of [Formula: see text] 90% when classifying active wave breaking in the test dataset. An example of a direct application of the method includes a statistical description of geometrical and kinematic properties of breaking waves. We expect that the present method and the associated dataset will be crucial for future research related to wave breaking in several areas of research, which include but are not limited to: improving operational forecast models, developing risk assessment and coastal management tools, and refining the retrieval of remotely sensed ocean properties.

| S-EPMC7878786 | biostudies-literature

Deep Neural Networks for Dental Implant System Classification.

Project description:In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.

| S-EPMC7407934 | biostudies-literature

Magnesium facilitates the healing of atypical femoral fractures: A single-cell transcriptomic study

Project description:We reported the application of single-cell mRNA sequencing to identify a unique population of fibroblasts that exits in the fracture callus of bisphosphonate-treated rats. Such unique population of fibroblasts prevented fracture healing by secreting ECM. Further, it was found that these ECM-secreting fibroblasts were enriched for myeloid genes, suggesting a bone marrow orgin. After fractures were treated with local CGRP injection or Magnesium based biometal, such population of fibroblasts was cleared off, resulting in facilitated fracture healing of bisphosphonate-treated rats.

2022-02-03 | GSE185988 | GEO

Atypical femoral fractures from bisphosphonate in cancer patients - Review.

Project description:Bisphosphonates are commonly used in patients with metastatic bone disease to prevent skeletal related events. Atypical femur fracture is a known complication of long-term bisphosphonate use but the incidence in cancer patients and pathogenesis are not well known. Several mechanisms of pathogenesis have been proposed including altered angiogenesis, altered bone mechanical properties, micro damage and bone remodeling suppression. Atypical femur fractures are atraumatic or minimally traumatic fractures in the sub trochanteric region or the femoral shaft. Awareness of atypical femur fractures is critical to diagnose and treat them in a timely manner. There is a paucity of data regarding the management of atypical femur fracture in patients with malignancy. Management options of atypical femur fractures include stopping bisphosphonates, initiating calcium/vitamin D supplementation and either surgery with internal fixation or conservative management. In the future, it will be important to explore the effect of continuous vs. intermittent exposure, cumulative dose and length of exposure on the incidence of this complication. Herein, we review the epidemiology, risk factors, management options and proposed mechanisms of pathogenesis of atypical femur fractures.

| S-EPMC6722257 | biostudies-literature

Deep Neural Networks for Classification of LC-MS Spectral Peaks.

Project description:Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has emerged as a valuable tool for biological discovery, capable of assaying thousands of diverse chemical entities in a single biospecimen. Processing of nontargeted LC-MS spectral data requires identification and isolation of true spectral features from the random, false noise peaks that comprise a significant portion of total signals, using inexact peak selection algorithms and time-consuming visual inspection of data. To increase the fidelity and speed of data processing, herein we establish, optimize, and evaluate a machine learning pipeline employing deep neural networks as well as a simpler multiple logistic regression model for classification of spectral features from nontargeted LC-MS metabolomics data. Machine learning-based approaches were found to remove up to 90% of false peaks from complex nontargeted LC-MS data sets without reducing true positive signals and exhibit excellent reproducibility across multiple data sets. Application of machine learning for nontargeted LC-MS-based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics data sets.

| S-EPMC7089603 | biostudies-literature

Periapical dental X-ray image classification using deep neural networks.

Project description:This paper studies the problem of detection of dental diseases. Dental problems affect the vast majority of the world's population. Caries, RCT (Root Canal Treatment), Abscess, Bone Loss, and missing teeth are some of the most common dental conditions that affect people of all ages all over the world. Delayed or incorrect diagnosis may result in mistreatment, affecting not only an individual's oral health but also his or her overall health, thereby making it an important research area in medicine and engineering. We propose a pipelined Deep Neural Network (DNN) approach to detect healthy and non-healthy periapical dental X-ray images. Even a minor enhancement or improvement in existing techniques can go a long way in providing significant health benefits in the medical field. This paper has made a successful attempt to contribute a different type of pipelined approach using AlexNet in this regard. The approach is trained on a large dataset of 16,000 dental X-ray images, correctly identifying healthy and non-healthy X-ray images. We use an optimized Convolutional Neural Networks and three state-of-the-art DNN models, namely Res-Net-18, ResNet-34, and AlexNet for disease classification. In our study, the AlexNet model outperforms the other models with an accuracy of 0.852. The precision, recall and F1 scores of AlexNet also surpass the other models with a score of 0.850 across all metrics. The area under ROC curve also signifies that both the false-positive rate and false-negative rate are low. We conclude that even with a big data set and raw X-ray pictures, the AlexNet model generalizes effectively to previously unseen data and can aid in the diagnosis of a variety of dental diseases.

| S-EPMC9483455 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data