AIMC Topic: Diagnosis, Computer-Assisted

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Why implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis.

EBioMedicine
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) algorithms have shown great promise in clinical medicine. Despite the increasing number of published algorithms, most remain unvalidated in real-world clinical settings. This study ai...

DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization.

Computers in biology and medicine
Neurologists often face challenges in identifying epileptic activities within multichannel EEG recordings, requiring extensive hours of analysis. Computer-aided diagnosis systems have been proposed to reduce manual inspection of EEG signals by neurol...

Large language models for accurate disease detection in electronic health records: the examples of crystal arthropathies.

RMD open
OBJECTIVES: We propose and test a framework to detect disease diagnosis using a recent large language model (LLM), Meta's Llama-3-8B, on French-language electronic health record (EHR) documents. Specifically, it focuses on detecting gout ('goutte' in...

MISDP: multi-task fusion visit interval for sequential diagnosis prediction.

BMC bioinformatics
BACKGROUNDS: Diagnostic prediction is a central application that spans various medical specialties and scenarios, sequential diagnosis prediction is the process of predicting future diagnoses based on patients' historical visits. Prior research has u...

A novel case-based reasoning system for explainable lung cancer diagnosis.

Computers in biology and medicine
Lung cancer is a leading cause of cancer death worldwide. The survival rate is generally higher when this disease is detected in its early stages. Advances in artificial intelligence (AI) have enabled the development of decision support systems that ...

Artificial intelligence-based computer-aided diagnosis for breast cancer detection on digital mammography in Hong Kong.

Hong Kong medical journal = Xianggang yi xue za zhi
INTRODUCTION: Research concerning artificial intelligence in breast cancer detection has primarily focused on population screening. However, Hong Kong lacks a population-based screening programme. This study aimed to evaluate the potential of artific...

Detection of Vitiligo Through Machine Learning and Computer-Aided Techniques: A Systematic Review.

BioMed research international
Vitiligo is a chronic skin damage disease, triggered by differential melanocyte death. Vitiligo (0.5%-1% of the population) is one of the most severe skin conditions. In general, the foundation of the condition of vitiligo remains gradual patchy los...

Multimodal autism detection: Deep hybrid model with improved feature level fusion.

Computer methods and programs in biomedicine
OBJECTIVE: Social communication difficulties are a characteristic of autism spectrum disorder (ASD), a neurodevelopmental condition. The earlier method of diagnosing autism largely relied on error-prone behavioral observation of symptoms. More intell...

Automated Bone Cancer Detection Using Deep Learning on X-Ray Images.

Surgical innovation
In recent days, bone cancer is a life-threatening health issue that can lead to death. However, physicians use CT-scan, X-rays, or MRI images to recognize bone cancer, but still require techniques to increase precision and reduce human labor. These m...

ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists' intentions.

Artificial intelligence in medicine
Using Deep Learning in computer-aided diagnosis systems has been of great interest due to its impressive performance in the general domain and medical domain. However, a notable challenge is the lack of explainability of many advanced models, which p...