AIMC Topic: Adult

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Oral and Gut Dysbiosis in Migraine: Oral Microbial Signatures as Biomarkers of Migraine.

Neurology(R) neuroimmunology & neuroinflammation
BACKGROUND AND OBJECTIVES: Emerging evidence suggests that oral health conditions may exacerbate migraine, and saliva is a potential source of biomarkers for migraine. The 3-way interaction of the oral-gut-brain axis has been implicated in several ne...

The implementation of computer-aided detection in an initial endoscopy training improves the quality measures of trainees' future colonoscopies: a retrospective cohort study.

Surgical endoscopy
INTRODUCTION: The implementation of computer-aided detection (CADe) systems has resulted in a growing number of young endoscopists being trained using AI-enhanced devices. The potential impact of AI-enhanced training on the trainees' future performan...

Landmark display system for laparoscopic inguinal hernia repair using artificial intelligence.

Surgical endoscopy
BACKGROUND: Chronic postoperative inguinal pain (CPIP) is a major complication of inguinal hernia repair and significantly affects patients' quality of life. Despite the widespread use of transabdominal preperitoneal repair (TAPP), CPIP still occurs....

AI-augmented differential diagnosis of granulomatous rosacea and lupus miliaris disseminatus faciei: A 23-year retrospective pilot study.

PloS one
Granulomatous rosacea (GR) and lupus miliaris disseminatus faciei (LMDF) exhibit overlapping clinical features, making their differentiation challenging. While histopathological examination remains the gold standard, it is invasive and time-consuming...

Improving a data mining based diagnostic support tool for rare diseases on the example of M. Fabry: Gender differences need to be taken into account.

PloS one
BACKGROUND: Rare diseases often present with a variety of clinical symptoms and therefore are challenging to diagnose. Fabry disease is an x-linked rare metabolic disorder. The severity of symptoms is usually different in men and women. Since therape...

Exploring data augmentation methods to enhance EEG measures for epilepsy seizure detection.

Computers in biology and medicine
Automatic seizure detection using machine learning can reduce the workload of clinicians in epilepsy diagnosis. However, the class imbalance between seizure and non-seizure data limits model performance. Data augmentation offers a solution, yet few s...

Deep Learning-Based Automated Detection of the Middle Cerebral Artery in Transcranial Doppler Ultrasound Examinations.

Ultrasound in medicine & biology
OBJECTIVE: Transcranial Doppler (TCD) ultrasound has significant clinical value for assessing cerebral hemodynamics, but its reliance on operator expertise limits broader clinical adoption. In this work, we present a lightweight real-time deep learni...

Evaluation of meibomian gland dysfunction with deep learning model considering different datasets and gland morphology.

Computers in biology and medicine
Meibomian gland dysfunction (MGD) is recognized as the primary cause of evaporative-type dry eye disease (DED). Diagnosis typically involves assessing meibomian gland (MG) morphology alongside symptom evaluation. Traditionally, experts manually grade...

The diagnostic model from semi-supervised cross modality transformation improved the distinguished ability of X-rays for pulmonary tuberculosis.

Clinical radiology
BACKGROUND: Early diagnosis of tuberculosis is particularly difficult in resource-poor areas. Traditional chest X-rays (CXR) have limited accuracy, while CT scans are costly and involve radiation exposure. The study aims to improve the diagnostic acc...