3,552 research outputs found
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
Skeletal bone age assessment is a common clinical practice to diagnose
endocrine and metabolic disorders in child development. In this paper, we
describe a fully automated deep learning approach to the problem of bone age
assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017.
The dataset for this competition is consisted of 12.6k radiological images of
left hand labeled by the bone age and sex of patients. Our approach utilizes
several deep learning architectures: U-Net, ResNet-50, and custom VGG-style
neural networks trained end-to-end. We use images of whole hands as well as
specific parts of a hand for both training and inference. This approach allows
us to measure importance of specific hand bones for the automated bone age
analysis. We further evaluate performance of the method in the context of
skeletal development stages. Our approach outperforms other common methods for
bone age assessment.Comment: 14 pages, 9 figure
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data
Knee osteoarthritis (OA) is the most common musculoskeletal disease without a
cure, and current treatment options are limited to symptomatic relief.
Prediction of OA progression is a very challenging and timely issue, and it
could, if resolved, accelerate the disease modifying drug development and
ultimately help to prevent millions of total joint replacement surgeries
performed annually. Here, we present a multi-modal machine learning-based OA
progression prediction model that utilizes raw radiographic data, clinical
examination results and previous medical history of the patient. We validated
this approach on an independent test set of 3,918 knee images from 2,129
subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81)
and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference
approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP
of 0.62 (0.60-0.64). The proposed method could significantly improve the
subject selection process for OA drug-development trials and help the
development of personalized therapeutic plans
Deep Learning for the Radiographic Detection of periodontal Bone Loss
We applied deep convolutional neural networks (CNNs) to detect periodontal bone loss (PBL) on panoramic dental radiographs. We synthesized a set of 2001 image segments from panoramic radiographs. Our reference test was the measured % of PBL. A deep feed-forward CNN was trained and validated via 10-times repeated group shuffling. Model architectures and hyperparameters were tuned using grid search. The final model was a seven-layer deep neural network, parameterized by a total number of 4,299,651 weights. For comparison, six dentists assessed the image segments for PBL. Averaged over 10 validation folds the mean (SD) classification accuracy of the CNN was 0.81 (0.02). Mean (SD) sensitivity and specificity were 0.81 (0.04), 0.81 (0.05), respectively. The mean (SD) accuracy of the dentists was 0.76 (0.06), but the CNN was not statistically significant superior compared to the examiners (p = 0.067/t-test). Mean sensitivity and specificity of the dentists was 0.92 (0.02) and 0.63 (0.14), respectively. A CNN trained on a limited amount of radiographic image segments showed at least similar discrimination ability as dentists for assessing PBL on panoramic radiographs. Dentists’ diagnostic efforts when using radiographs may be reduced by applying machine-learning based technologies
The Bionic Radiologist: avoiding blurry pictures and providing greater insights
Radiology images and reports have long been digitalized. However, the potential of the more than 3.6 billion radiology
examinations performed annually worldwide has largely gone unused in the effort to digitally transform health care. The Bionic
Radiologist is a concept that combines humanity and digitalization for better health care integration of radiology. At a practical
level, this concept will achieve critical goals: (1) testing decisions being made scientifically on the basis of disease probabilities and
patient preferences; (2) image analysis done consistently at any time and at any site; and (3) treatment suggestions that are closely
linked to imaging results and are seamlessly integrated with other information. The Bionic Radiologist will thus help avoiding missed
care opportunities, will provide continuous learning in the work process, and will also allow more time for radiologists’ primary
roles: interacting with patients and referring physicians. To achieve that potential, one has to cope with many implementation
barriers at both the individual and institutional levels. These include: reluctance to delegate decision making, a possible decrease in
image interpretation knowledge and the perception that patient safety and trust are at stake. To facilitate implementation of the
Bionic Radiologist the following will be helpful: uncertainty quantifications for suggestions, shared decision making, changes in
organizational culture and leadership style, maintained expertise through continuous learning systems for training, and role
development of the involved experts. With the support of the Bionic Radiologist, disparities are reduced and the delivery of care is
provided in a humane and personalized fashion
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