AIMC Topic: Humans

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The Mason-Alberta Phonetic Segmenter: a forced alignment system based on deep neural networks and interpolation.

Phonetica
Given an orthographic transcription, forced alignment systems automatically determine boundaries between segments in speech, facilitating the use of large corpora. In the present paper, we introduce a neural network-based forced alignment system, the...

Common Critiques and Recommendations for Studies in Neurology Using Machine Learning Methods.

Neurology
Machine learning (ML) methods are becoming more prevalent in the neurology literature as alternatives to traditional statistical methods to address challenges in the analysis of modern data sets. Despite the increase in the popularity of ML methods i...

Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke but associated with high mortality rates. Hematoma expansion (HE) is an increase in the bleeding that affects 30%-38% of hemorrhagic stroke patients. I...

Predicting Short Time-to-Crime Guns: a Machine Learning Analysis of California Transaction Records (2010-2021).

Journal of urban health : bulletin of the New York Academy of Medicine
Gun-related crime continues to be an urgent public health and safety problem in cities across the US. A key question is: how are firearms diverted from the legal retail market into the hands of gun offenders? With close to 8 million legal firearm tra...

Reconstruction of patient-specific confounders in AI-based radiologic image interpretation using generative pretraining.

Cell reports. Medicine
Reliably detecting potentially misleading patterns in automated diagnostic assistance systems, such as those powered by artificial intelligence (AI), is crucial for instilling user trust and ensuring reliability. Current techniques fall short in visu...

Deep learning models map rapid plant species changes from citizen science and remote sensing data.

Proceedings of the National Academy of Sciences of the United States of America
Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learni...

A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics.

Tomography (Ann Arbor, Mich.)
Pneumonia caused by novel coronavirus is an acute respiratory infectious disease. Its rapid spread in a short period of time has brought great challenges for global public health. The use of deep learning and radiomics methods can effectively disting...

Antimicrobial activity of compounds identified by artificial intelligence discovery engine targeting enzymes involved in Neisseria gonorrhoeae peptidoglycan metabolism.

Biological research
BACKGROUND: Neisseria gonorrhoeae (Ng) causes the sexually transmitted disease gonorrhoea. There are no vaccines and infections are treated principally with antibiotics. However, gonococci rapidly develop resistance to every antibiotic class used and...

Application of machine learning techniques in the diagnosis of endometriosis.

BMC women's health
OBJECTIVE: The aim of this study is to assess the use of machine learning methodologies in the diagnosis of endometriosis (EM).

Data privacy-aware machine learning approach in pancreatic cancer diagnosis.

BMC medical informatics and decision making
PROBLEM: Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The five-year survival rate after diagnosis is less than 10%. However, if diagnosed early, the five-year survival rate can reac...