Breast cancer classification in point-of-care ultrasound imaging-the impact of training data.

Purpose: The survival rate of breast cancer for women in low- and middle-income countries is poor compared with that in high-income countries. Point-of-care ultrasound (POCUS) combined with deep learning could potentially be a suitable solution enabling early detection of breast cancer. We aim to improve a classification network dedicated to classifying POCUS images by comparing different techniques for increasing the amount of training data.

Artificial intelligence for detection of prostate cancer in biopsies during active surveillance.

Objectives: To evaluate a cancer detecting artificial intelligence (AI) algorithm on serial biopsies in patients with prostate cancer on active surveillance (AS).

Patients And Methods: A total of 180 patients in the Prostate Cancer Research International Active Surveillance (PRIAS) cohort were prospectively monitored using pre-defined criteria. Diagnostic and re-biopsy slides from 2011 to 2020 (n = 4744) were scanned and analysed by an in-house AI-based cancer detection algorithm.

A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables.

Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET composites from low-cost and non-invasive features, e.

The use of accelerometer bracelets to evaluate arm motor function over a stroke rehabilitation period - an explorative observational study.

Background: Assessments of arm motor function are usually based on clinical examinations or self-reported rating scales. Wrist-worn accelerometers can be a good complement to measure movement patterns after stroke. Currently there is limited knowledge of how accelerometry correlate to clinically used scales.

Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases.

Cerebrospinal fluid (CSF) biomarkers reflect brain pathophysiology and are used extensively in translational research as well as in clinical practice for diagnosis of neurological diseases, e.g., Alzheimer's disease (AD).

Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera.

Purpose: Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning.

Methods: 546 patients (67% men) undergoing stress 99mTc-tetrofosmin MPI in a CZT camera in the upright and supine position were included (1092 MPIs). Patients were divided into two groups: ICA group included 271 patients who performed an ICA within 6 months of MPI and a control group with 275 patients with low pre-test probability for CAD and a normal MPI.

Detection of Unilateral Arm Paresis after Stroke by Wearable Accelerometers and Machine Learning.

Recent advances in stroke treatment have provided effective tools to successfully treat ischemic stroke, but still a majority of patients are not treated due to late arrival to hospital. With modern stroke treatment, earlier arrival would greatly improve the overall treatment results. This prospective study was performed to asses the capability of bilateral accelerometers worn in bracelets 24/7 to detect unilateral arm paralysis, a hallmark symptom of stroke, early enough to receive treatment.

An Artificial Intelligence-based Support Tool for Automation and Standardisation of Gleason Grading in Prostate Biopsies.

Background: Gleason grading is the standard diagnostic method for prostate cancer and is essential for determining prognosis and treatment. The dearth of expert pathologists, the inter- and intraobserver variability, as well as the labour intensity of Gleason grading all necessitate the development of a user-friendly tool for robust standardisation.

Objective: To develop an artificial intelligence (AI) algorithm, based on machine learning and convolutional neural networks, as a tool for improved standardisation in Gleason grading in prostate cancer biopsies.

Cellular traction forces: a useful parameter in cancer research.

The search for new cancer biomarkers is essential for fundamental research, diagnostics, as well as for patient treatment and monitoring. Whereas most cancer biomarkers are biomolecules, an increasing number of studies show that mechanical cues are promising biomarker candidates. Although cell deformability has been shown to be a possible cancer biomarker, cellular forces as cancer biomarkers have been left largely unexplored.

Action sequencing in the spontaneous swimming behavior of zebrafish larvae - implications for drug development.

All motile organisms need to organize their motor output to obtain functional goals. In vertebrates, natural behaviors are generally composed of a relatively large set of motor components which in turn are combined into a rich repertoire of complex actions. It is therefore an experimental challenge to investigate the organizational principles of natural behaviors.

Smooth pursuit detection in binocular eye-tracking data with automatic video-based performance evaluation.

An increasing number of researchers record binocular eye-tracking signals from participants viewing moving stimuli, but the majority of event-detection algorithms are monocular and do not consider smooth pursuit movements. The purposes of the present study are to develop an algorithm that discriminates between fixations and smooth pursuit movements in binocular eye-tracking signals and to evaluate its performance using an automated video-based strategy. The proposed algorithm uses a clustering approach that takes both spatial and temporal aspects of the binocular eye-tracking signal into account, and is evaluated using a novel video-based evaluation strategy based on automatically detected moving objects in the video stimuli.

Exploratory study of EEG burst characteristics in preterm infants.

In this paper, we study machine learning techniques and features of electroencephalography activity bursts for predicting outcome in extremely preterm infants. It was previously shown that the distribution of interburst interval durations predicts clinical outcome, but in previous work the information within the bursts has been neglected. In this paper, we perform exploratory analysis of feature extraction of burst characteristics and use machine learning techniques to show that such features could be used for outcome prediction.

A pilot study of facial, cranial and brain MRI morphometry in men with schizophrenia: part 2.

This pilot study applies a new 3D morphometric MR method to test the hypothesis that men with schizophrenia (vs. controls) have deviant facial shapes and landmark relations in cranio/facial/brain (CFB) regions. This constitutes Part 2 of paired articles in this issue of Psychiatry Research: Neuroimaging, in which Part 1 presents the new method in detail.