A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study.

Background: Anxiety is one of the leading causes of mental health disability around the world. Currently, a majority of the population who experience anxiety go undiagnosed or untreated. New and innovative ways of diagnosing and monitoring anxiety have emerged using smartphone sensor-based monitoring as a metric for the management of anxiety.

A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study.

Background: Depression is a major global cause of morbidity, an economic burden, and the greatest health challenge leading to chronic disability. Mobile monitoring of mental conditions has long been a sought-after metric to overcome the problems associated with the screening, diagnosis, and monitoring of depression and its heterogeneous presentation. The widespread availability of smartphones has made it possible to use their data to generate digital behavioral models that can be used for both clinical and remote screening and monitoring purposes.

Automated identification of cephalometric landmarks:

Objectives: To compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Only-Look-Once version 3 (YOLOv3), with those identified by human examiners.

Materials And Methods: The YOLOv3 algorithm was implemented with custom modifications and trained on 1028 cephalograms. A total of 80 landmarks comprising two vertical reference points and 46 hard tissue and 32 soft tissue landmarks were identified.

Automated identification of cephalometric landmarks: .

Objective: To compare the accuracy and computational efficiency of two of the latest deep-learning algorithms for automatic identification of cephalometric landmarks.

Materials And Methods: A total of 1028 cephalometric radiographic images were selected as learning data that trained You-Only-Look-Once version 3 (YOLOv3) and Single Shot Multibox Detector (SSD) methods. The number of target labeling was 80 landmarks.

Steer-PROP: a GRASE-PROPELLER sequence with interecho steering gradient pulses.

Purpose: This study demonstrates a novel PROPELLER (periodically rotated overlapping parallel lines with enhanced reconstruction) pulse sequence, termed Steer-PROP, based on gradient and spin echo (GRASE), to reduce the imaging times and address phase errors inherent to GRASE. The study also illustrates the feasibility of using Steer-PROP as an alternative to single-shot echo planar imaging (SS-EPI) to produce distortion-free diffusion images in all imaging planes.

Methods: Steer-PROP uses a series of blip gradient pulses to produce N (N = 3-5) adjacent k-space blades in each repetition time, where N is the number of gradient echoes in a GRASE sequence.

Anomalous diffusion measured by a twice-refocused spin echo pulse sequence: analysis using fractional order calculus.

Purpose: To theoretically develop and experimentally validate a formulism based on a fractional order calculus (FC) diffusion model to characterize anomalous diffusion in brain tissues measured with a twice-refocused spin-echo (TRSE) pulse sequence.

Materials And Methods: The FC diffusion model is the fractional order generalization of the Bloch-Torrey equation. Using this model, an analytical expression was derived to describe the diffusion-induced signal attenuation in a TRSE pulse sequence.

Diffusion tensor imaging study of white matter fiber tracts in pediatric bipolar disorder and attention-deficit/hyperactivity disorder.

Background: To investigate microstructure of white matter fiber tracts in pediatric bipolar disorder (PBD) and attention-deficit/hyperactivity disorder (ADHD).

Methods: A diffusion tensor imaging (DTI) study was conducted at 3 Tesla on age- and IQ-matched children and adolescents with PBD (n = 13), ADHD (n = 13), and healthy control subjects (HC) (n = 15). Three DTI parameters, fractional anisotropy (FA), apparent diffusion coefficient (ADC), and regional fiber coherence index (r-FCI), were examined in eight fiber tracts: anterior corona radiata (ACR), anterior limb of the internal capsule (ALIC), superior region of the internal capsule (SRI), posterior limb of the internal capsule (PLIC), superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), cingulum (CG), and splenium (SP).