AIMC Topic: Diagnosis, Computer-Assisted

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An Overview of Explainable AI Studies in the Prediction of Sepsis Onset and Sepsis Mortality.

Studies in health technology and informatics
Explainable artificial intelligence (AI) focuses on developing models and algorithms that provide transparent and interpretable insights into decision-making processes. By elucidating the reasoning behind AI-driven diagnoses and treatment recommendat...

Artificial Intelligence System for Automated Breast Cancer Detection in Pathology in Burkina Faso: Methodology Overview.

Studies in health technology and informatics
The introduction of artificial intelligence (AI) in breast cancer diagnosis in Burkina Faso represents a significant advancement in the field of healthcare. Faced with the public health issue posed by breast cancer, this study focuses on the use of A...

Explainable Machine Learning Based Prediction of Severity of Heart Failure Using Primary Electronic Health Records.

Studies in health technology and informatics
Heart Failure (HF) is a life-threatening condition. It affects more than 64 million people worldwide. Early diagnosis of HF is extremely crucial. In this study, we propose utilization of machine learning (ML) models to predict severity of HF from pri...

[Research Progress of Artificial Intelligence in Prostate Cancer Diagnosis Application].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
With the continuous advancement of artificial intelligence in the field of prostate cancer research, numerous studies have shown that AI performance can rival that of physicians. This review examines the recent applications and developments of AI in ...

Breast Cancer Tissue Classification from Multiple Annotators using Chained Deep Learning Approaches.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Breast cancer is one of the principal causes of cancer death worldwide. The biopsy diagnosis is non-trivial, and specialists often disagree on the final diagnosis. Thus, Computer-aided Diagnosis-(CAD) systems favor the efficiency of this process whil...

Diagnosis of Pneumoconiosis with Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Pneumoconiosis encompasses a group of lung diseases caused by inhaling dust particles. Frequently recognized as an occupational disease, it primarily affects workers in the mining industry. This paper details the use of machine learning algorithms to...

Dynamic multi-hypergraph structure learning for disease diagnosis on multimodal data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
With its superior capability in complex data modeling, hypergraph computation is a powerful tool for many applications. In this work, we propose using hypergraph computation for disease prediction. Hypergraphs allow for the representation of higher-o...

LightIED: Explainable AI with Light CNN for Interictal Epileptiform Discharge Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Interictal epileptic discharge (IED) detection from electroencephalography (EEG) is an important but difficult step in the epilepsy diagnosis. To reduce the workload of doctors, some diagnostic auxiliary methods based on deep learning have been propo...

Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their potential for clinical translation. Data augmentation (DA) could address this limitation, but the utili...

A nested cross validation approach to machine learning model performance evaluation on a small dataset for Creutzfeldt-Jakob disease diagnosis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The use of machine learning (ML) to diagnose neurological diseases has become increasingly popular. However, some rare neurodegenerative diseases such as Creutzfeldt-Jakob disease (CJD) suffer from the way that the traditional diagnosis relying abnor...