Large-Scale Study on AI's Impact on Identifying Chest Radiographs with No Actionable Disease in Outpatient Imaging.

Rationale And Objectives: Given the high volume of chest radiographs, radiologists frequently encounter heavy workloads. In outpatient imaging, a substantial portion of chest radiographs show no actionable findings. Automatically identifying these cases could improve efficiency by facilitating shorter reading workflows.

Contrastive self-supervised learning from 100 million medical images with optional supervision.

Purpose: Building accurate and robust artificial intelligence systems for medical image assessment requires the creation of large sets of annotated training examples. However, constructing such datasets is very costly due to the complex nature of annotation tasks, which often require expert knowledge (e.g.

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning.

Background: Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED.

Methods: This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.

Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19.

Rapid prognostication of COVID-19 patients is important for efficient resource allocation. We evaluated the relative prognostic value of baseline clinical variables (CVs), quantitative human-read chest CT (qCT), and AI-read chest radiograph (qCXR) airspace disease (AD) in predicting severe COVID-19. We retrospectively selected 131 COVID-19 patients (SARS-CoV-2 positive, March to October, 2020) at a tertiary hospital in the United States, who underwent chest CT and CXR within 48 hr of initial presentation.

A Semi-Automated Method for Measuring Biofilm Accumulation on the Teeth Using Quantitative Light-Induced Fluorescence in Dogs and Cats.

Oral health conditions (eg, plaque, calculus, gingivitis) cause morbidity and pain in companion animals. Thus, developing technologies that can ameliorate the accumulation of oral biofilm, a critical factor in the progression of these conditions, is vital. Quantitative light-induced fluorescence (QLF) is a method to quantify oral substrate accumulation, and therefore, it can assess biofilm attenuation of different products.

Artificial Intelligence in Chest Radiography Reporting Accuracy: Added Clinical Value in the Emergency Unit Setting Without 24/7 Radiology Coverage.

Objectives: Chest radiographs (CXRs) are commonly performed in emergency units (EUs), but the interpretation requires radiology experience. We developed an artificial intelligence (AI) system (precommercial) that aims to mimic board-certified radiologists' (BCRs') performance and can therefore support non-radiology residents (NRRs) in clinical settings lacking 24/7 radiology coverage. We validated by quantifying the clinical value of our AI system for radiology residents (RRs) and EU-experienced NRRs in a clinically representative EU setting.

Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training.

Objectives: Diagnostic accuracy of artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs (CXR) is limited by the noisy annotation quality of public training data and confounding thoracic tubes (TT). We hypothesize that in-image annotations of the dehiscent visceral pleura for algorithm training boosts algorithm's performance and suppresses confounders.

Methods: Our single-center evaluation cohort of 3062 supine CXRs includes 760 PTX-positive cases with radiological annotations of PTX size and inserted TTs.

Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground Truth.

Objectives: The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1) train a convolutional neural network (CNN) to quantify AD on paired chest radiographs (CXRs) and CTs, and (2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19.

Materials And Methods: We retrospectively selected a cohort of 86 COVID-19 patients (with positive reverse transcriptase-polymerase chain reaction test results) from March to May 2020 at a tertiary hospital in the northeastern United States, who underwent chest CT and CXR within 48 hours. The ground-truth volumetric percentage of COVID-19-related AD (POv) was established by manual AD segmentation on CT.

Quantifying and leveraging predictive uncertainty for medical image assessment.

The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest radiography, where there is a high inter-rater variability in the detection and classification of abnormalities. This is largely due to inconclusive evidence in the data or subjective definitions of disease appearance.

Ultrasound-Based Phenotyping of Lateral Ventricles to Predict Hydrocephalus Outcome in Premature Neonates.

Objective: Prediction of post-hemorrhagic hydrocephalus (PHH) outcome-i.e., whether it requires intervention or not-in premature neonates using cranial ultrasound (CUS) images is challenging.

Validation of a new predictive model to improve risk stratification in bronchopulmonary dysplasia.

We need a better risk stratification system for the increasing number of survivors of extreme prematurity suffering the most severe forms of bronchopulmonary dysplasia (BPD). However, there is still a paucity of studies providing scientific evidence to guide future updates of BPD severity definitions. Our goal was to validate a new predictive model for BPD severity that incorporates respiratory assessments beyond 36 weeks postmenstrual age (PMA).

A Generic Approach to Lung Field Segmentation From Chest Radiographs Using Deep Space and Shape Learning.

Computer-aided diagnosis (CAD) techniques for lung field segmentation from chest radiographs (CXR) have been proposed for adult cohorts, but rarely for pediatric subjects. Statistical shape models (SSMs), the workhorse of most state-of-the-art CXR-based lung field segmentation methods, do not efficiently accommodate shape variation of the lung field during the pediatric developmental stages. The main contributions of our work are: 1) a generic lung field segmentation framework from CXR accommodating large shape variation for adult and pediatric cohorts; 2) a deep representation learning detection mechanism, ensemble space learning, for robust object localization; and 3) marginal shape deep learning for the shape deformation parameter estimation.

Ventricular shape evaluation on early ultrasound predicts post-hemorrhagic hydrocephalus.

Background: To compare the ability of ventricular morphology on cranial ultrasound (CUS) versus standard clinical variables to predict the need for temporizing cerebrospinal fluid drainage in newborns with intraventricular hemorrhage (IVH).

Method: This is a retrospective study of newborns (gestational age <29 weeks) diagnosed with IVH. Clinical variables known to increase the risk for post-hemorrhagic hydrocephalus were collected.

Automatic Segmentation of Neonatal Ventricles from Cranial Ultrasound for Prediction of Intraventricular Hemorrhage Outcome.

Intraventricular hemorrhage (IVH) followed by post hemorrhagic hydrocephalus (PHH) in premature neonates is one of the recognized reasons of brain injury in newborns. Cranial ultrasound (CUS) is a noninvasive imaging tool that has been used widely to diagnose and monitor neonates with IVH. In our previous work, we showed the potential of quantitative morphological analysis of lateral ventricles from early CUS to predict the PHH outcome in neonates with IVH.

Cranial ultrasound-based prediction of post hemorrhagic hydrocephalus outcome in premature neonates with intraventricular hemorrhage.

Premature neonates with intraventricular hemorrhage (IVH) followed by post hemorrhagic hydrocephalus (PHH) are at high risk for brain injury. Cranial ultrasound (CUS) is used for monitoring of premature neonates during the first weeks after birth to identify IVH and follow the progression to PHH. However, the lack of a standardized method for CUS evaluation has led to significant variability in decision making regarding treatment.

Marginal Shape Deep Learning: Applications to Pediatric Lung Field Segmentation.

Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, localization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters.

Optic pathway glioma volume predicts retinal axon degeneration in neurofibromatosis type 1.

Objective: To determine whether tumor size is associated with retinal nerve fiber layer (RNFL) thickness, a measure of axonal degeneration and an established biomarker of visual impairment in children with optic pathway gliomas (OPGs) secondary to neurofibromatosis type 1 (NF1).

Methods: Children with NF1-OPGs involving the optic nerve (extension into the chiasm and tracts permitted) who underwent both volumetric MRI analysis and optical coherence tomography (OCT) within 2 weeks of each other were included. Volumetric measurement of the entire anterior visual pathway (AVP; optic nerve, chiasm, and tract) was performed using high-resolution T1-weighted MRI.

Quantitative MRI criteria for optic pathway enlargement in neurofibromatosis type 1.

Objective: To determine quantitative size thresholds for enlargement of the optic nerve, chiasm, and tract in children with neurofibromatosis type 1 (NF1).

Methods: Children 0.5-18.

Deep Learning Guided Partitioned Shape Model for Anterior Visual Pathway Segmentation.

Analysis of cranial nerve systems, such as the anterior visual pathway (AVP), from MRI sequences is challenging due to their thin long architecture, structural variations along the path, and low contrast with adjacent anatomic structures. Segmentation of a pathologic AVP (e.g.

Severity quantification of pediatric viral respiratory illnesses in chest X-ray images.

Accurate assessment of severity of viral respiratory illnesses (VRIs) allows early interventions to prevent morbidity and mortality in young children. This paper proposes a novel imaging biomarker framework with chest X-ray image for assessing VRI's severity in infants, developed specifically to meet the distinct challenges for pediatric population. The proposed framework integrates three novel technical contributions: a) lung segmentation using weighted partitioned active shape model, b) obtrusive object removal using graph cut segmentation with asymmetry constraint, and c) severity quantification using information-theoretic heterogeneity measures.

Automatic tissue characterization of air trapping in chest radiographs using deep neural networks.

Significant progress has been made in recent years for computer-aided diagnosis of abnormal pulmonary textures from computed tomography (CT) images. Similar initiatives in chest radiographs (CXR), the common modality for pulmonary diagnosis, are much less developed. CXR are fast, cost effective and low-radiation solution to diagnosis over CT.

Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends.

The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods.

Computer-aided pulmonary image analysis in small animal models.

Purpose: To develop an automated pulmonary image analysis framework for infectious lung diseases in small animal models.

Methods: The authors describe a novel pathological lung and airway segmentation method for small animals. The proposed framework includes identification of abnormal imaging patterns pertaining to infectious lung diseases.