AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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FT-FEDTL: A fine-tuned feature-extracted deep transfer learning model for multi-class microwave-based brain tumor classification.

Computers in biology and medicine
The microwave brain imaging (MBI) system is an emerging technology used to detect brain tumors in their early stages. Multi-class microwave-based brain tumor (MBT) identification and classification are crucial due to the tumor's patterns and shape. M...

Cost-effectiveness and cost-utility of community-based blinding fundus diseases screening with artificial intelligence: A modelling study from Shanghai, China.

Computers in biology and medicine
BACKGROUND: With application of artificial intelligence (AI) in the disease screening, process reengineering occurred simultaneously. Whether process reengineering deserves special emphasis in AI implementation in the community-based blinding fundus ...

Transformative artificial intelligence in gastric cancer: Advancements in diagnostic techniques.

Computers in biology and medicine
Gastric cancer represents a significant global health challenge with elevated incidence and mortality rates, highlighting the need for advancements in diagnostic and therapeutic strategies. This review paper addresses the critical need for a thorough...

Machine learning model identifies genetic predictors of cisplatin-induced ototoxicity in CERS6 and TLR4.

Computers in biology and medicine
BACKGROUND: Cisplatin-induced ototoxicity remains a significant concern in pediatric cancer treatment due to its permanent impact on quality of life. Previously, genetic association analyses have been performed to detect genetic variants associated w...

Effective deep-learning brain MRI super resolution using simulated training data.

Computers in biology and medicine
BACKGROUND: In the field of medical imaging, high-resolution (HR) magnetic resonance imaging (MRI) is essential for accurate disease diagnosis and analysis. However, HR imaging is prone to artifacts and is not universally available. Consequently, low...

Automated detection and labeling of posterior teeth in dental bitewing X-rays using deep learning.

Computers in biology and medicine
Standardized tooth numbering is crucial in dentistry for accurate recordkeeping, targeted procedures, and effective communication in both clinical and forensic contexts. However, conventional manual methods are prone to errors, time-consuming, and su...

Comparative accuracy of artificial intelligence chatbots in pulpal and periradicular diagnosis: A cross-sectional study.

Computers in biology and medicine
OBJECTIVES: This study aimed to evaluate the diagnostic accuracy and treatment recommendation performance of four artificial intelligence chatbots in fictional pulpal and periradicular disease cases. Additionally, it investigated response consistency...

CK-ATTnet: Medical image segmentation network based on convolutional kernel attention.

Computers in biology and medicine
The medical image partition model has a wide range of application prospects in medical diagnosis and treatment and has become an important auxiliary method to improve the diagnostic level by medical imaging analysis. After the feature extraction abil...

Predicting ergonomic risk among laboratory technicians using a Cheetah Optimizer-Integrated Deep Convolutional Neural Network.

Computers in biology and medicine
Medical laboratory technicians play a significant role in clinical units by conducting diagnostic tests and analyses. However, their job nature involving repetitive motions, prolonged standing or sitting, etc., leads to potential ergonomic risks. Thi...

LTMSegnet: Lightweight multi-scale medical image segmentation combining Transformer and MLP.

Computers in biology and medicine
Medical image segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. In recent years, neural network-based methods have improved in terms of segmentation accuracy and become the mainstream in the field ...