Evaluating the Performance and Bias of Natural Language Processing Tools in Labeling Chest Radiograph Reports.

Background Natural language processing (NLP) is commonly used to annotate radiology datasets for training deep learning (DL) models. However, the accuracy and potential biases of these NLP methods have not been thoroughly investigated, particularly across different demographic groups. Purpose To evaluate the accuracy and demographic bias of four NLP radiology report labeling tools on two chest radiograph datasets.

The "Halo Appearance" of Shear Wave Elastography in the Setting of Fat Necrosis: A Potentially Useful Sonographic Discriminator.

Objective: To describe the morphologic sonographic appearances and frequency of the "halo sign" in the setting of fat necrosis on shear wave elastography (SWE).

Methods: Patients with clinically suspected fat necrosis were prospectively scanned using SWE in addition to standard gray-scale and Doppler images. Cases were qualitatively grouped into one of three sonographic appearances: focal hypoechoic lesion with increased internal tissue stiffness ("focal stiffness"), focal hypoechoic lesion with isoechoic or hyperechoic periphery demonstrating increased tissue stiffness relative to the central hypoechoic lesion ("halo stiffness"), heterogeneously echogenic lesion with diffusely increased stiffness ("heterogeneous stiffness").

Artificial intelligence improves resident detection of pediatric and young adult upper extremity fractures.

Purpose: We wished to evaluate if an open-source artificial intelligence (AI) algorithm ( https://www.childfx.com ) could improve performance of (1) subspecialized musculoskeletal radiologists, (2) radiology residents, and (3) pediatric residents in detecting pediatric and young adult upper extremity fractures.

Lower Extremity Growth according to AI Automated Femorotibial Length Measurement on Slot-Scanning Radiographs in Pediatric Patients.

Background Commonly used pediatric lower extremity growth standards are based on small, dated data sets. Artificial intelligence (AI) enables creation of updated growth standards. Purpose To train an AI model using standing slot-scanning radiographs in a racially diverse data set of pediatric patients to measure lower extremity length and to compare expected growth curves derived using AI measurements to those of the conventional Anderson-Green method.

Utility of MRI for Patients 45 Years Old and Older With Hip or Knee Pain: A Systematic Review.

. MRI utility for patients 45 years old and older with hip or knee pain is not well established. .

Artificial intelligence to identify fractures on pediatric and young adult upper extremity radiographs.

Background: Pediatric fractures are challenging to identify given the different response of the pediatric skeleton to injury compared to adults, and most artificial intelligence (AI) fracture detection work has focused on adults.

Objective: Develop and transparently share an AI model capable of detecting a range of pediatric upper extremity fractures.

Materials And Methods: In total, 58,846 upper extremity radiographs (finger/hand, wrist/forearm, elbow, humerus, shoulder/clavicle) from 14,873 pediatric and young adult patients were divided into train (n = 12,232 patients), tune (n = 1,307), internal test (n = 819), and external test (n = 515) splits.

Clinical Applications, Challenges, and Recommendations for Artificial Intelligence in Musculoskeletal and Soft-Tissue Ultrasound: Expert Panel Narrative Review.

Artificial intelligence (AI) is increasingly used in clinical practice for musculoskeletal imaging tasks, such as disease diagnosis and image reconstruction. AI applications in musculoskeletal imaging have focused primarily on radiography, CT, and MRI. Although musculoskeletal ultrasound stands to benefit from AI in similar ways, such applications have been relatively underdeveloped.

Visceral Adiposity Independently Predicts Time to Flare in Inflammatory Bowel Disease but Body Mass Index Does Not.

Background: Obesity is associated with progression of inflammatory bowel disease (IBD). Visceral adiposity may be a more meaningful measure of obesity compared with traditional measures such as body mass index (BMI). This study compared visceral adiposity vs BMI as predictors of time to IBD flare among patients with Crohn's disease and ulcerative colitis.

Detecting pediatric wrist fractures using deep-learning-based object detection.

Background: Missed fractures are the leading cause of diagnostic error in the emergency department, and fractures of pediatric bones, particularly subtle wrist fractures, can be misidentified because of their varying characteristics and responses to injury.

Objective: This study evaluated the utility of an object detection deep learning framework for classifying pediatric wrist fractures as positive or negative for fracture, including subtle buckle fractures of the distal radius, and evaluated the performance of this algorithm as augmentation to trainee radiograph interpretation.

Materials And Methods: We obtained 395 posteroanterior wrist radiographs from unique pediatric patients (65% positive for fracture, 30% positive for distal radial buckle fracture) and divided them into train (n = 229), tune (n = 41) and test (n = 125) sets.

Artificial Intelligence (AI) for Fracture Diagnosis: An Overview of Current Products and Considerations for Clinical Adoption, From the Special Series on AI Applications.

Fractures are common injuries that can be difficult to diagnose, with missed fractures accounting for most misdiagnoses in the emergency department. Artificial intelligence (AI) and, specifically, deep learning have shown a strong ability to accurately detect fractures and augment the performance of radiologists in proof-of-concept research settings. Although the number of real-world AI products available for clinical use continues to increase, guidance for practicing radiologists in the adoption of this new technology is limited.

Inferring pediatric knee skeletal maturity from MRI using deep learning.

Purpose: Many children who undergo MR of the knee to evaluate traumatic injury may not undergo a separate dedicated evaluation of their skeletal maturity, and we wished to investigate how accurately skeletal maturity could be automatically inferred from knee MRI using deep learning to offer this additional information to clinicians.

Materials And Methods: Retrospective data from 894 studies from 783 patients were obtained (mean age 13.1 years, 47% female).

Risk stratification for hydronephrosis in the evaluation of acute kidney injury: a cross-sectional analysis.

Objective: To validate an existing clinical decision support tool to risk-stratify patients with acute kidney injury (AKI) for hydronephrosis and compare the risk stratification framework with nephrology consultant recommendations.

Setting: Cross-sectional study of hospitalised adults with AKI who had a renal ultrasound (RUS) ordered at a large, tertiary, academic medical centre.

Participants: Two hundred and eighty-one patients were included in the study cohort.

Identifying Factors Important to Patients for Resuming Elective Imaging During the COVID-19 Pandemic.

Purpose: To identify factors important to patients for their return to elective imaging during the coronavirus disease 2019 (COVID-19) pandemic.

Methods: In all, 249 patients had elective MRIs postponed from March 23, 2020, to April 24, 2020, because of the COVID-19 pandemic. Of these patients, 99 completed a 22-question survey about living arrangement and health care follow-up, effect of imaging postponement, safety of imaging, and factors important for elective imaging.

Detecting insertion, substitution, and deletion errors in radiology reports using neural sequence-to-sequence models.

Background: Errors in grammar, spelling, and usage in radiology reports are common. To automatically detect inappropriate insertions, deletions, and substitutions of words in radiology reports, we proposed using a neural sequence-to-sequence (seq2seq) model.

Methods: Head CT and chest radiograph reports from Mount Sinai Hospital (MSH) (n=61,722 and 818,978, respectively), Mount Sinai Queens (MSQ) (n=30,145 and 194,309, respectively) and MIMIC-III (n=32,259 and 54,685) were converted into sentences.

An attention based deep learning model of clinical events in the intensive care unit.

This study trained long short-term memory (LSTM) recurrent neural networks (RNNs) incorporating an attention mechanism to predict daily sepsis, myocardial infarction (MI), and vancomycin antibiotic administration over two week patient ICU courses in the MIMIC-III dataset. These models achieved next-day predictive AUC of 0.876 for sepsis, 0.

Combination of Active Transfer Learning and Natural Language Processing to Improve Liver Volumetry Using Surrogate Metrics with Deep Learning.

Purpose: To determine if weakly supervised learning with surrogate metrics and active transfer learning can hasten clinical deployment of deep learning models.

Materials And Methods: By leveraging Liver Tumor Segmentation (LiTS) challenge 2017 public data ( = 131 studies), natural language processing of reports, and an active learning method, a model was trained to segment livers on 239 retrospectively collected portal venous phase abdominal CT studies obtained between January 1, 2014, and December 31, 2016. Absolute volume differences between predicted and originally reported liver volumes were used to guide active learning and assess accuracy.

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study.

Background: There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task.

CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis.

Motivation: Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems.

Automated deep-neural-network surveillance of cranial images for acute neurologic events.

Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcomes and preserving neurologic function-'time is brain'. Although these disorders are often recognizable by their symptoms, the critical means of their diagnosis is rapid imaging. Computer-aided surveillance of acute neurologic events in cranial imaging has the potential to triage radiology workflow, thus decreasing time to treatment and improving outcomes.

Safety and Outcomes of Transradial Access in Patients with International Normalized Ratio 1.5 or above.

Purpose: To examine the safety and outcomes for patients undergoing transradial noncoronary interventions with international normalized ratio (INR) ≥1.5.

Materials And Methods: A retrospective review of 2,271 transradial access (TRA) cases performed from July 2012 to July 2016 was conducted.

Natural Language-based Machine Learning Models for the Annotation of Clinical Radiology Reports.

Purpose To compare different methods for generating features from radiology reports and to develop a method to automatically identify findings in these reports. Materials and Methods In this study, 96 303 head computed tomography (CT) reports were obtained. The linguistic complexity of these reports was compared with that of alternative corpora.

Measuring the Degree of Unmatched Patient Records in a Health Information Exchange Using Exact Matching.

Background: Health information exchange (HIE) facilitates the exchange of patient information across different healthcare organizations. To match patient records across sites, HIEs usually rely on a master patient index (MPI), a database responsible for determining which medical records at different healthcare facilities belong to the same patient. A single patient's records may be improperly split across multiple profiles in the MPI.

Identifying homelessness using health information exchange data.

Background: Homeless patients experience poor health outcomes and consume a disproportionate amount of health care resources compared with domiciled patients. There is increasing interest in the federal government in providing care coordination for homeless patients, which will require a systematic way of identifying these individuals.

Objective: We analyzed address data from Healthix, a New York City-based health information exchange, to identify patterns that could indicate homelessness.