AIMC Topic: Diagnosis, Computer-Assisted

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Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study.

Medicine
The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve accuracy, a computer-assisted diagnosis system is used for more effective pneumoco...

[A survey on the application of convolutional neural networks in the diagnosis of occupational pneumoconiosis].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Pneumoconiosis ranks first among the newly-emerged occupational diseases reported annually in China, and imaging diagnosis is still one of the main clinical diagnostic methods. However, manual reading of films requires high level of doctors, and it i...

[Medical image segmentation data augmentation method based on channel weight and data-efficient features].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
In computer-aided medical diagnosis, obtaining labeled medical image data is expensive, while there is a high demand for model interpretability. However, most deep learning models currently require a large amount of data and lack interpretability. To...

Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis.

Human brain mapping
Novel features derived from imaging and artificial intelligence systems are commonly coupled to construct computer-aided diagnosis (CAD) systems that are intended as clinical support tools or for investigation of complex biological patterns. This stu...

A systematic review on deep learning-based automated cancer diagnosis models.

Journal of cellular and molecular medicine
Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated dia...

AI for Detection of Tuberculosis: Implications for Global Health.

Radiology. Artificial intelligence
Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the...

RDguru: An Intelligent Agent for Rare Diseases.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Large language models (LLMs) have shown great promise in clinical medicine, but their adoption in real-world settings has been limited by their tendency to generate incorrect and sometimes even toxic statements. This study presents a reliable rare di...

A Hybrid 2D Gaussian Filter and Deep Learning Approach with Visualization of Class Activation for Automatic Lung and Colon Cancer Diagnosis.

Technology in cancer research & treatment
Cancer is a significant public health issue due to its high prevalence and lethality, particularly lung and colon cancers, which account for over a quarter of all cancer cases. This study aims to enhance the detection rate of lung and colon cancer by...

Parametric optimization and comparative study of machine learning and deep learning algorithms for breast cancer diagnosis.

Breast disease
Breast Cancer is the leading form of cancer found in women and a major cause of increased mortality rates among them. However, manual diagnosis of the disease is time-consuming and often limited by the availability of screening systems. Thus, there i...

Using AI and ML to Predict Autism Spectrum Disorder.

IEEE pulse
Autism spectrum disorder is a condition that showcases the potential usefulness of artificial intelligence (AI) and machine learning (ML). This is an area of great need, according to Dennis Wall, Ph.D., professor of pediatrics and biomedical data sci...