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.

Preliminary evidence from a prospective DTI study suggests a posterior-to-anterior pattern of recovery in college athletes with sports-related concussion.

Objectives: We compared the integrity of white matter (WM) microstructure to the course of recovery in athletes who sustained one sports-related concussion (SRC), assessing individual longitudinal changes in WM fiber tracts following SRC using pre- and post-injury measurements.

Materials And Methods: Baseline diffusion tensor imaging (DTI) scans and neuropsychological tests were collected on 53 varsity contact-sport college athletes. Participants (n = 13) who subsequently sustained an SRC underwent DTI scans and neuropsychological testing at 2 days, 2 weeks, and 2 months following injury.

Prospective clinical assessment using Sideline Concussion Assessment Tool-2 testing in the evaluation of sport-related concussion in college athletes.

Objective: To evaluate the utility of the Sideline Concussion Assessment Tool (SCAT)-2 in collegiate athletes with sport-related concussion.

Design: Prospective cross-sectional study with baseline testing and serial repeat testing after concussion in contact sport athletes and non-concussed control athletes.

Setting: Division I University.

A longitudinal diffusion tensor imaging study assessing white matter fiber tracts after sports-related concussion.

The extent of structural injury in sports-related concussion (SRC) is central to the course of recovery, long-term effects, and the decision to return to play. In the present longitudinal study, we used diffusion tensor imaging (DTI) to assess white matter (WM) fiber tract integrity within 2 days, 2 weeks, and 2 months of concussive injury. Participants were right-handed male varsity contact-sport athletes (20.

Persistent differences in patterns of brain activation after sports-related concussion: a longitudinal functional magnetic resonance imaging study.

Avoiding recurrent injury in sports-related concussion (SRC) requires understanding the neural mechanisms involved during the time of recovery after injury. The decision for return-to-play is one of the most difficult responsibilities facing the physician, and so far this decision has been based primarily on neurological examination, symptom checklists, and neuropsychological (NP) testing. Functional magnetic resonance imaging (fMRI) may be an additional, more objective tool to assess the severity and recovery of function after concussion.

Automatic cropping of MRI rat brain volumes using pulse coupled neural networks.

The Pulse Coupled Neural Network (PCNN) was developed by Eckhorn to model the observed synchronization of neural assemblies in the visual cortex of small mammals such as a cat. In this paper we show the use of the PCNN as an image segmentation strategy to crop MR images of rat brain volumes. We then show the use of the associated PCNN image 'signature' to automate the brain cropping process with a trained artificial neural network.

Imaging the neural circuitry and chemical control of aggressive motivation.

Background: With the advent of functional magnetic resonance imaging (fMRI) in awake animals it is possible to resolve patterns of neuronal activity across the entire brain with high spatial and temporal resolution. Synchronized changes in neuronal activity across multiple brain areas can be viewed as functional neuroanatomical circuits coordinating the thoughts, memories and emotions for particular behaviors. To this end, fMRI in conscious rats combined with 3D computational analysis was used to identifying the putative distributed neural circuit involved in aggressive motivation and how this circuit is affected by drugs that block aggressive behavior.