Deep Learning for Automated Classification of Hip Hardware on Radiographs.

Purpose: To develop a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs, which can be clinically implemented to decrease radiologist workload and improve consistency among radiology reports.

Materials And Methods: Pelvic and hip radiographs from 4279 studies in 1073 patients were retrospectively obtained and reviewed by musculoskeletal radiologists. Two convolutional neural networks, EfficientNet-B4 and NFNet-F3, were trained to perform the image classification task into the following most represented categories: no hardware, total hip arthroplasty (THA), hemiarthroplasty, intramedullary nail, femoral neck cannulated screws, dynamic hip screw, lateral blade/plate, THA with additional femoral fixation, and post-infectious hip.

Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports.

Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting.

Clinical utility of accelerated MAVRIC-SL with robust-PCA compared to conventional MAVRIC-SL in evaluation of total hip arthroplasties.

Objective: To compare the diagnostic performance of a conventional metal artifact suppression sequence MAVRIC-SL (multi-acquisition variable-resonance image combination selective) and a novel 2.6-fold faster sequence employing robust principal component analysis (RPCA), in the MR evaluation of hip implants at 3 T.

Materials And Methods: Thirty-six total hip implants in 25 patients were scanned at 3 T using a conventional MAVRIC-SL proton density-weighted sequence and an RPCA MAVRIC-SL proton density-weighted sequence.

Combined 5-minute double-echo in steady-state with separated echoes and 2-minute proton-density-weighted 2D FSE sequence for comprehensive whole-joint knee MRI assessment.

Background: Clinical knee MRI protocols require upwards of 15 minutes of scan time.

Purpose/hypothesis: To compare the imaging appearance of knee abnormalities depicted with a 5-minute 3D double-echo in steady-state (DESS) sequence with separate echo images, with that of a routine clinical knee MRI protocol. A secondary goal was to compare the imaging appearance of knee abnormalities depicted with 5-minute DESS paired with a 2-minute coronal proton-density fat-saturated (PDFS) sequence.

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.

Background: Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses.

Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: Application to bone tumor radiographs.

Background: The majority of current medical CBIR systems perform retrieval based only on "imaging signatures" generated by extracting pixel-level quantitative features, and only rarely has a feedback mechanism been incorporated to improve retrieval performance. In addition, current medical CBIR approaches do not routinely incorporate semantic terms that model the user's high-level expectations, and this can limit CBIR performance.

Method: We propose a retrieval framework that exploits a hybrid feature space (HFS) that is built by integrating low-level image features and high-level semantic terms, through rounds of relevance feedback (RF) and performs similarity-based retrieval to support semi-automatic image interpretation.

Bone Tumor Diagnosis Using a Naïve Bayesian Model of Demographic and Radiographic Features.

Because many bone tumors have a variety of appearances and are uncommon, few radiologists develop sufficient expertise to guide optimal management. Bayesian inference can guide decision-making by computing probabilities of multiple diagnoses to generate a differential. We built and validated a naïve Bayes machine (NBM) that processes 18 demographic and radiographic features.

Computerized Prediction of Radiological Observations Based on Quantitative Feature Analysis: Initial Experience in Liver Lesions.

We propose a computerized framework that, given a region of interest (ROI) circumscribing a lesion, not only predicts radiological observations related to the lesion characteristics with 83.2% average prediction accuracy but also derives explicit association between low-level imaging features and high-level semantic terms by exploiting their statistical correlation. Such direct association between semantic concepts and low-level imaging features can be leveraged to build a powerful annotation system for radiological images that not only allows the computer to infer the semantics from diverse medical images and run automatic reasoning for making diagnostic decision but also provides "human-interpretable explanation" of the system output to facilitate better end user understanding of computer-based diagnostic decisions.

Detection and prevalence of variant sciatic nerve anatomy in relation to the piriformis muscle on MRI.

Objective: To determine whether known variant anatomical relationships between the sciatic nerve and piriformis muscle can be identified on routine MRI studies of the hip and to establish their imaging prevalence.

Methods: Hip MRI studies acquired over a period of 4 years at two medical centers underwent retrospective interpretation. Anatomical relationship between the sciatic nerve and the piriformis muscle was categorized according to the Beaton and Anson classification system.

Adaptive local window for level set segmentation of CT and MRI liver lesions.

We propose a novel method, the adaptive local window, for improving level set segmentation technique. The window is estimated separately for each contour point, over iterations of the segmentation process, and for each individual object. Our method considers the object scale, the spatial texture, and the changes of the energy functional over iterations.

Chondral Rib Fractures in Professional American Football: Two Cases and Current Practice Patterns Among NFL Team Physicians.

Background: Although a recognized and discussed injury, chondral rib fractures in professional American football have not been previously reported in the literature. There currently exists no consensus on how to identify and treat these injuries or the expected return to play for the athlete.

Purpose: To present 2 cases of chondral rib injuries in the National Football League (NFL) and discuss the current practice patterns for management of these injuries among the NFL team physicians.

Weighted locality-constrained linear coding for lesion classification in CT images.

Computed tomography is a popular imaging modality for detecting abnormalities associated with abdominal organs such as the liver, kidney and uterus. In this paper, we propose a novel weighted locality-constrained linear coding (LLC) method followed by a weighted max-pooling method to classify liver lesions into three classes: cysts, metastases, hemangiomas. We first divide the lesions into same-size patches.

Piriformis Syndrome With Variant Sciatic Nerve Anatomy: A Case Report.

A 68-year-old male long distance runner presented with low back and left buttock pain, which eventually progressed to severe and debilitating pain, intermittently radiating to the posterior thigh and foot. A comprehensive workup ruled out possible spine or hip causes of his symptoms. A pelvic magnetic resonance imaging neurogram with complex oblique planes through the piriformis demonstrated variant anatomy of the left sciatic nerve consistent with the clinical diagnosis of piriformis syndrome.

Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions.

The bag-of-visual-words (BoVW) method with construction of a single dictionary of visual words has been used previously for a variety of classification tasks in medical imaging, including the diagnosis of liver lesions. In this paper, we describe a novel method for automated diagnosis of liver lesions in portal-phase computed tomography (CT) images that improves over single-dictionary BoVW methods by using an image patch representation of the interior and boundary regions of the lesions. Our approach captures characteristics of the lesion margin and of the lesion interior by creating two separate dictionaries for the margin and the interior regions of lesions ("dual dictionaries" of visual words).

Ultrasound-guided musculoskeletal interventions in American football: 18 years of experience.

Objective: Myotendinous strains, contusions, and hematomas are common injuries in American football. Along with ligament sprains and inflammatory disorders, musculoskeletal injuries often result in lost participation time. This article summarizes 18 years of experience with 128 ultrasound-guided drainages and injections in 69 football players with 88 injuries.

Classification of hypervascular liver lesions based on hepatic artery and portal vein blood supply coefficients calculated from triphasic CT scans.

Perfusion CT of the liver typically involves scanning the liver at least 20 times, resulting in a large radiation dose. We developed and validated a simplified model of tumor blood supply that can be applied to standard triphasic scans and evaluated whether this can be used to distinguish benign and malignant liver lesions. Triphasic CTs of 46 malignant and 32 benign liver lesions were analyzed.

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications.

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance.

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations.

Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images.

A picture is worth a thousand words: needs assessment for multimedia radiology reports in a large tertiary care medical center.

Rationale And Objectives: Radiology reports are the major, and often only, means of communication between radiologists and their referring clinicians. The purposes of this study are to identify referring physicians' preferences about radiology reports and to quantify their perceived value of multimedia reports (with embedded images) compared with narrative text reports.

Materials And Methods: We contacted 1800 attending physicians from a range of specialties at large tertiary care medical center via e-mail and a hospital newsletter linking to a 24-question electronic survey between July and November 2012.

Modeling perceptual similarity measures in CT images of focal liver lesions.

Motivation: A gold standard for perceptual similarity in medical images is vital to content-based image retrieval, but inter-reader variability complicates development. Our objective was to develop a statistical model that predicts the number of readers (N) necessary to achieve acceptable levels of variability.

Materials And Methods: We collected 3 radiologists' ratings of the perceptual similarity of 171 pairs of CT images of focal liver lesions rated on a 9-point scale.

Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval.

Purpose: To develop a method to quantify the margin sharpness of lesions on CT and to evaluate it in simulations and CT scans of liver and lung lesions.

Methods: The authors computed two attributes of margin sharpness: the intensity difference between a lesion and its surroundings, and the sharpness of the intensity transition across the lesion boundary. These two attributes were extracted from sigmoid curves fitted along lines automatically drawn orthogonal to the lesion margin.

A comprehensive descriptor of shape: method and application to content-based retrieval of similar appearing lesions in medical images.

We have developed a method to quantify the shape of liver lesions in CT images and to evaluate its performance for retrieval of images with similarly-shaped lesions. We employed a machine learning method to combine several shape descriptors and defined similarity measures for a pair of shapes as a weighted combination of distances calculated based on each feature. We created a dataset of 144 simulated shapes and established several reference standards for similarity and computed the optimal weights so that the retrieval result agrees best with the reference standard.