AIMC Topic: Proof of Concept Study

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Inferring a network from dynamical signals at its nodes.

PLoS computational biology
We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they ...

Deep Learning for Voice Gender Identification: Proof-of-concept for Gender-Affirming Voice Care.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: The need for gender-affirming voice care has been increasing in the transgender population in the last decade. Currently, objective treatment outcome measurements are lacking to assess the success of these interventions. This s...

Artificial intelligence automates and augments baseline impedance measurements from pH-impedance studies in gastroesophageal reflux disease.

Journal of gastroenterology
BACKGROUND: Artificial intelligence (AI) has potential to streamline interpretation of pH-impedance studies. In this exploratory observational cohort study, we determined feasibility of automated AI extraction of baseline impedance (AIBI) and evaluat...

Automated analysis and detection of abnormalities in transaxial anatomical cardiovascular magnetic resonance images: a proof of concept study with potential to optimize image acquisition.

The international journal of cardiovascular imaging
The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early ...

Developing a Machine Learning Algorithm for Identifying Abnormal Urothelial Cells: A Feasibility Study.

Acta cytologica
INTRODUCTION: Urine cytology plays an important role in diagnosing urothelial carcinoma (UC). However, urine cytology interpretation is subjective and difficult. Morphogo (ALAB, Boston, MA, USA), equipped with automatic acquisition and scanning, opti...

Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma.

Virchows Archiv : an international journal of pathology
In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic changes. We developed a deep learning algorithm to detect MYC rearrangement in scanned histological slide...

Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.

Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology
BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms ...

A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates.

mBio
Variation in the genome of , an important pathogen, can have dramatic impacts on the bacterium's ability to cause disease. We therefore asked whether it was possible to predict the virulence of isolates based on their genomic content. We applied a m...

Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas.

The Annals of otology, rhinology, and laryngology
OBJECTIVE: Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annot...

Developing a neurally informed ontology of creativity measurement.

NeuroImage
A central challenge for creativity research-as for all areas of experimental psychology and cognitive neuroscience-is to establish a mapping between constructs and measures (i.e., identifying a set of tasks that best captures a set of creative abilit...