AIMC Topic: Algorithms

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PenoMeter: a machine learning and algorithmic tool to advance Peyronie's disease assessment.

The journal of sexual medicine
BACKGROUND: Peyronie's disease curvature assessment is a critical step for patient assessment; however, tools for objective, unbiased, and reproducible quantification are currently limited.

Rapid pathologic grading-based diagnosis of esophageal squamous cell carcinoma Raman spectroscopy and a deep learning algorithm.

World journal of gastroenterology
BACKGROUND: Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer. Many molecular genetic changes are associated with its occurrence. Raman spectroscopy has become a new method for the early diagnosis of tumors becau...

Artificial neural networks to predict the presence of Neosporosis in cattle.

Mathematical biosciences and engineering : MBE
The prediction of bovine infectious diseases is a constant challenge as generally, only laboratory data is available not allowing the study of their relationship with each disease's risk factors. The diseases neosporosis and bovine viral diarrhea, wh...

Using Machine Learning to Predict Survival in Patients with Metastatic Castration-Resistant Prostate Cancer.

Studies in health technology and informatics
Non-specific clinical biomarkers have been shown to help identify prognostic risks in cancer patients. However, the accuracy of prognostic biomarkers for predicting survival in patients with metastatic castration-resistant prostate cancer (mCRPC) sti...

Using C4.5 Algorithm to Gain Insights on Stakeholder Engagement and Use of Artificial Intelligence on Social Media in Dementia Caregiving Disparity Research.

Studies in health technology and informatics
We applied machine learning techniques to build models that predict perceived risks and benefits of using artificial intelligence (AI) algorithms to recruit African American informal caregivers for clinical trials and general health disparity researc...

Artificial intelligence-assisted approach to assessing bowel wall thickness in pediatric inflammatory bowel disease using intestinal ultrasound images.

Journal of Crohn's & colitis
BACKGROUND AND AIM: Intestinal ultrasound (IUS) potentially spares patients from repeated endoscopies under sedation and eliminates the need for alternative imaging modalities like magnetic resonance enterography and computed tomography enterography ...

Robust Diagnosis of Acute Bacterial and Viral Infections via Host Gene Expression Rank-Based Ensemble Machine Learning Algorithm: A Multi-Cohort Model Development and Validation Study.

Clinical chemistry
BACKGROUND: The accurate and prompt diagnosis of infections is essential for improving patient outcomes and preventing bacterial drug resistance. Host gene expression profiling as an approach to infection diagnosis holds great potential in assisting ...

Deep Learning Model for Diagnosing and Classifying Subtypes of Chronic Pulmonary Aspergillosis in Chest CT.

Mycoses
BACKGROUND: Diagnosing chronic pulmonary aspergillosis (CPA) and its subtypes is essential for treatment and prognosis. In clinical practice, inexperienced doctors may overlook the presence of CPA due to overreliance on radiological results. Applying...

AUGUR-AIM: Clinical validation of an artificial intelligence indocyanine green fluorescence angiography expert representer.

Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland
AIM: Recent randomized controlled trials and meta-analyses have demonstrated a reduction in the anastomotic leak rate when indocyanine green fluorescence angiography (ICGFA) is used versus when it is not in colorectal resections. We have previously d...

RRM-TransUNet: Deep-Learning Driven Interactive Model for Precise Pancreas Segmentation in CT Images.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Pancreatic diseases such as cancer and pancreatitis pose significant health risks. Early detection requires precise segmentation results. Fully automatic segmentation algorithms cannot integrate clinical expertise and correct output error...