AIMC Topic: Humans

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Differentiation Between Glioblastoma and Metastatic Disease on Conventional MRI Imaging Using 3D-Convolutional Neural Networks: Model Development and Validation.

Academic radiology
RATIONALE AND OBJECTIVES: Imaging-based differentiation between glioblastoma (GB) and brain metastases (BM) remains challenging. Our aim was to evaluate the performance of 3D-convolutional neural networks (CNN) to address this binary classification p...

DCDA: CircRNA-Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder.

Interdisciplinary sciences, computational life sciences
Circular RNA is a single-stranded RNA with a closed-loop structure. In recent years, academic research has revealed that circular RNAs play critical roles in biological processes and are related to human diseases. The discovery of potential circRNAs ...

Application of natural language processing to post-structuring of rectal cancer MRI reports.

Clinical radiology
AIM: To evaluate a natural language processing (NLP) system for extracting structured information from the free-form text of rectal cancer magnetic resonance imaging (MRI) reports written in Chinese.

ChatGPT's Ability to Assist with Clinical Documentation: A Randomized Controlled Trial.

The Journal of the American Academy of Orthopaedic Surgeons
INTRODUCTION: Clinical documentation is a critical aspect of health care that enables healthcare providers to communicate effectively with each other and maintain accurate patient care records. Artificial intelligence tools, such as chatbots and virt...

Long- and Short-Term Memory Model of Cotton Price Index Volatility Risk Based on Explainable Artificial Intelligence.

Big data
Market uncertainty greatly interferes with the decisions and plans of market participants, thus increasing the risk of decision-making, leading to compromised interests of decision-makers. Cotton price index (hereinafter referred to as cotton price) ...

Using expert-reviewed CSAM to train CNNs and its anthropological analysis.

Journal of forensic and legal medicine
Machine learning methods for the identification of child sexual abuse materials (CSAM) have been previously studied, however, they have serious limitations. Firstly, the training sets used to train the appropriate machine learning algorithms were not...

DeepSSM: A blueprint for image-to-shape deep learning models.

Medical image analysis
Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. Statistical analysis of shapes requires consistent shape representation across samples in shape cohort. Establishing this re...

Creation of a Novel, Race-Adjusted, and Risk-Adapted Scoring System to Predict Positive Surgical Margins and Prolonged Operative Time During Robotic Radical Prostatectomy.

Journal of endourology
To compare racial differences and pelvis dimensions between Caucasians and African Americans (AAs) and to develop a risk calculator and scoring system to predict the risk of prolonged operative time and presence of positive surgical margins (PSM) ba...

Language Artificial Intelligences' Communicative Performance Quantified Through the Gricean Conversation Theory.

Cyberpsychology, behavior and social networking
This study pragmatically investigates an artificial intelligence (AI) speaker (AIS)'s verbal communicative performance based on real AI-human conversation data. Specifically, this study explores Grice's conversation theory, which enables the categori...