Evaluating progress in automatic chest X-ray radiology report generation.

Artificial intelligence (AI) models for automatic generation of narrative radiology reports from images have the potential to enhance efficiency and reduce the workload of radiologists. However, evaluating the correctness of these reports requires metrics that can capture clinically pertinent differences. In this study, we investigate the alignment between automated metrics and radiologists' scoring of errors in report generation.

Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks.

Objective: Automatic MR imaging segmentation of the prostate provides relevant clinical benefits for prostate cancer evaluation such as calculation of automated PSA density and other critical imaging biomarkers. Further, automated T2-weighted image segmentation of central-transition zone (CZ-TZ), peripheral zone (PZ), and seminal vesicle (SV) can help to evaluate clinically significant cancer following the PI-RADS v2.1 guidelines.

A novel abnormality annotation database for COVID-19 affected frontal lung X-rays.

Consistent clinical observations of characteristic findings of COVID-19 pneumonia on chest X-rays have attracted the research community to strive to provide a fast and reliable method for screening suspected patients. Several machine learning algorithms have been proposed to find the abnormalities in the lungs using chest X-rays specific to COVID-19 pneumonia and distinguish them from other etiologies of pneumonia. However, despite the enormous magnitude of the pandemic, there are very few instances of public databases of COVID-19 pneumonia, and to the best of our knowledge, there is no database with annotation of abnormalities on the chest X-rays of COVID-19 affected patients.

A review on investigation of the basic contrast mechanism underlying multidimensional diffusion MRI in assessment of neurological disorders.

Introduction: Multidimensional diffusion MRI (MDD MRI) is a novel diffusion technique that uses advanced gradient waveforms for microstructural tissue characterization to provide information about average rate, anisotropy and orientation of the diffusion and to disentangle the signal fraction from specific cell types i.e., elongated cells, isotropic cells and free water.

Dapagliflozin Improves Body Fat Patterning, and Hepatic and Pancreatic Fat in Patients With Type 2 Diabetes in North India.

Context: Excess hepatic and pancreatic fat may contribute to hyperglycemia.

Objective: The objective of this study was to examine the effect of dapagliflozin (an SGLT2 inhibitor) on anthropometric profile, liver, and pancreatic fat in patients with type 2 diabetes mellitus (T2DM).

Methods: This is an observational interventional paired study design without a control group.

Clinical Explainability Failure (CEF) & Explainability Failure Ratio (EFR) - Changing the Way We Validate Classification Algorithms.

Adoption of Artificial Intelligence (AI) algorithms into the clinical realm will depend on their inherent trustworthiness, which is built not only by robust validation studies but is also deeply linked to the explainability and interpretability of the algorithms. Most validation studies for medical imaging AI report the performance of algorithms on study-level labels and lay little emphasis on measuring the accuracy of explanations generated by these algorithms in the form of heat maps or bounding boxes, especially in true positive cases. We propose a new metric - Explainability Failure Ratio (EFR) - derived from Clinical Explainability Failure (CEF) to address this gap in AI evaluation.

Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays.

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets.

Model for in-vivo estimation of stiffness of tibiofemoral joint using MR imaging and FEM analysis.

Background: Appropriate structural and material properties are essential for finite-element-modeling (FEM). In knee FEM, structural information could extract through 3D-imaging, but the individual subject's tissue material properties are inaccessible.

Purpose: The current study's purpose was to develop a methodology to estimate the subject-specific stiffness of the tibiofemoral joint using finite-element-analysis (FEA) and MRI data of knee joint with and without load.

The Algorithmic Audit: Working with Vendors to Validate Radiology-AI Algorithms-How We Do It.

There is a plethora of Artificial Intelligence (AI) tools that are being developed around the world aiming at either speeding up or improving the accuracy of radiologists. It is essential for radiologists to work with the developers of such algorithms to determine true clinical utility and risks associated with these algorithms. We present a framework, called an Algorithmic Audit, for working with the developers of such algorithms to test and improve the performance of the algorithms.

Unboxing AI - Radiological Insights Into a Deep Neural Network for Lung Nodule Characterization.

Rationale And Objectives: To explain predictions of a deep residual convolutional network for characterization of lung nodule by analyzing heat maps.

Materials And Methods: A 20-layer deep residual CNN was trained on 1245 Chest CTs from National Lung Screening Trial (NLST) trial to predict the malignancy risk of a nodule. We used occlusion to systematically block regions of a nodule and map drops in malignancy risk score to generate clinical attribution heatmaps on 103 nodules from Lung Image Database Consortium image collection and Image Database Resource Initiative (LIDC-IDRI) dataset, which were analyzed by a thoracic radiologist.

Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study.

Background: Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. We aimed to develop and validate a set of deep learning algorithms for automated detection of the following key findings from these scans: intracranial haemorrhage and its types (ie, intraparenchymal, intraventricular, subdural, extradural, and subarachnoid); calvarial fractures; midline shift; and mass effect.

Methods: We retrospectively collected a dataset containing 313 318 head CT scans together with their clinical reports from around 20 centres in India between Jan 1, 2011, and June 1, 2017.

Low-Grade Central Osteosarcoma: Report of Two Unusual Morphologic Variants.

Low-grade central osteosarcoma (LGCO) is a rare subtype of osteosarcoma, constituting < 2% of all osteosarcomas. If not treated appropriately, the tumor can recur with higher-grade disease. We report two cases of low-grade central osteosarcoma with unusual morphologic features and belonging to different age groups.

Hydatid cyst of parotid: Report of unusual cytological findings extending the cytomorphological spectrum.

Hydatid disease is a zoonotic infestation caused by larval cestode of genus Echinococcus. Cystic form of this infection mostly involves liver and lung. Hydatid disease of the parotid gland is very rare even in endemic areas and is often clinically mistaken for parotid tumors or cysts.