Alternative Medicine

Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems.

Skin cancer is among the most prevalent types of malignancy all over the global and is strongly associated with the patient's prognosis and the accuracy of the initial diagnosis. Clinical examination of skin lesions is a key aspect that is important in the assessment of skin disease but comes with some drawbacks mainly with interpretational aspects, time-consuming and healthare expenditure. Skin c...

Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading global health challenge, especially in low-resource settings. Accurate diagnosis from chest X-rays is critical yet challenging due to subtle manifestations of TB, particularly in its early stages. Traditional computational methods, primarily using basic convolutional neural networks (CNNs), often require extensive pre-proce...

Generating accurate sex estimation from hand X-ray images using AI deep-learning techniques: A study of limited bone regions.

Hand bone structure provides valuable features for sex estimation. This research introduces a novel approach using Artificial Intelligence (AI), speci...

Biofilm-mediated infections; novel therapeutic approaches and harnessing artificial intelligence for early detection and treatment of biofilm-associated infections.

A biofilm is a group of bacteria that have self-produced a matrix and are grouped together in a dense population. By resisting the host's immune syste...

Developing a deep learning model for the automated monitoring of acupuncture needle insertion: enhancing safety in traditional acupuncture practices.

BACKGROUND: Acupuncture is a widely practiced traditional therapy, yet safety concerns, particularly needle breakage and retention, remain critical is...

Liver lesion segmentation in ultrasound: A benchmark and a baseline network.

Accurate liver lesion segmentation in ultrasound is a challenging task due to high speckle noise, ambiguous lesion boundaries, and inhomogeneous inten...

Exploring the significance of the frontal lobe for diagnosis of schizophrenia using explainable artificial intelligence and group level analysis.

Schizophrenia (SZ) is a complex mental disorder characterized by a profound disruption in cognition and emotion, often resulting in a distorted percep...

MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning.

Spatially resolved transcriptomics (SRT) simultaneously measure spatial location, histology images, and transcriptional profiles of cells or regions i...

The dawn of the revolution that will allow us to precisely describe how microbiomes function.

The community of microorganisms inhabiting a specific environment, such as the human gut - including bacteria, fungi, archaea, viruses, protozoa, and ...

PROPERMAB: an integrative framework for prediction of antibody developability using machine learning.

Selection of lead therapeutic molecules is often driven predominantly by pharmacological efficacy and safety. Candidate developability, such as biophy...

Current methods in explainable artificial intelligence and future prospects for integrative physiology.

Explainable artificial intelligence (XAI) is gaining importance in physiological research, where artificial intelligence is now used as an analytical ...

Improving Malaria diagnosis through interpretable customized CNNs architectures.

Malaria, which is spread via female Anopheles mosquitoes and is brought on by the Plasmodium parasite, persists as a serious illness, especially in ar...

SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis.

Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the st...

Education and Training Assessment and Artificial Intelligence. A Pragmatic Guide for Educators.

The emergence of ChatGPT and similar new Generative AI tools has created concern about the validity of many current assessment methods in higher educa...

IPNet: An Interpretable Network With Progressive Loss for Whole-Stage Colorectal Disease Diagnosis.

Colorectal cancer plays a dominant role in cancer-related deaths, primarily due to the absence of obvious early-stage symptoms. Whole-stage colorectal...

Towards unbiased skin cancer classification using deep feature fusion.

This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially ...

Operational Advantages of Novel Strategies Supported by Portability and Artificial Intelligence for Breast Cancer Screening in Low-Resource Rural Areas: Opportunities to Address Health Inequities and Vulnerability.

Early detection of breast cancer plays a crucial role in reducing the number of cases diagnosed at advanced stages, thereby lowering the high healthca...

Triptolide suppresses IL-1β-induced expression of interleukin-8 by inhibiting ROS-Mediated ERK, AP-1, and NF-κB molecules in human gastric cancer AGS cells.

Triptolide, the major component of Chinese herbal medicine Tripterygium wilfordii Hook F, possesses potent anticancer and anti-inflammatory effects. I...

Differentiation of Citri Reticulatae Pericarpium varieties via HPLC fingerprinting of polysaccharides combined with machine learning.

To accurately and reliably distinguish different varieties of Citri Reticulatae Pericarpium (CRP), we propose a novel classification strategy combinin...

Comment on 'CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images'.

An analysis of the methodology used by the authors of the commented article is presented and errors related to data preparation are pointed out.