AIMC Topic: Artificial Intelligence

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Efficient Evolving Deep Ensemble Medical Image Captioning Network.

IEEE journal of biomedical and health informatics
With the advancement in artificial intelligence (AI) based E-healthcare applications, the role of automated diagnosis of various diseases has increased at a rapid rate. However, most of the existing diagnosis models provide results in a binary fashio...

Anti-Jamming Strategy for Federated Learning in Internet of Medical Things: A Game Approach.

IEEE journal of biomedical and health informatics
Federated learning (FL) is a new dawn of artificial intelligence (AI), in which machine learning models are constructed in a distributed manner while communicating only model parameters between a centralized aggregator and client internet-of-medical-...

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey.

IEEE journal of biomedical and health informatics
Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using internet of medical things (IoMT) devices. However, due to the centralized training a...

Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review.

Journal of healthcare engineering
BACKGROUND: Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to w...

A Learning Robust and Discriminative Shape Descriptor for Plant Species Identification.

IEEE/ACM transactions on computational biology and bioinformatics
Plant identification based on leaf images is a widely concerned application field in artificial intelligence and botany. The key problem is extracting robust discriminative features from leaf images and assigning a measure of similarity. This study p...

Artificial Intelligence-Driven Image Analysis of Bacterial Cells and Biofilms.

IEEE/ACM transactions on computational biology and bioinformatics
The current study explores an artificial intelligence framework for measuring the structural features from microscopy images of the bacterial biofilms. Desulfovibrio alaskensis G20 (DA-G20) grown on mild steel surfaces is used as a model for sulfate ...

Clinical utility of a deep-learning mortality prediction model for cardiac surgery decision making.

The Journal of thoracic and cardiovascular surgery
OBJECTIVES: The aim of this study using decision curve analysis (DCA) was to evaluate the clinical utility of a deep-learning mortality prediction model for cardiac surgery decision making compared with the European System for Cardiac Operative Risk ...

Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI).

European radiology
This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the essentials for understanding and implementing Artificial intelligence (AI) in clinical practice in thoracic radiology departments. This document discusses...

SAGES video acquisition framework-analysis of available OR recording technologies by the SAGES AI task force.

Surgical endoscopy
BACKGROUND: Surgical video recording provides the opportunity to acquire intraoperative data that can subsequently be used for a variety of quality improvement, research, and educational applications. Various recording devices are available for stand...

Computer-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning With an Ensemble Model.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Screening of lymph node metastases in colorectal cancer (CRC) can be a cumbersome task, but it is amenable to artificial intelligence (AI)-assisted diagnostic solution. Here, we propose a deep learning-based workflow for the evaluation of CRC lymph n...