Preventive Care

Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening.

BACKGROUND: Deep learning breast cancer risk models demonstrate improved accuracy compared with traditional risk models but have not been prospectively tested. We compared the accuracy of a deep learning risk score derived from the patient's prior mammogram to traditional risk scores to prospectively identify patients with cancer in a cohort due for screening.

A Framework for Automating Psychiatric Distress Screening in Ophthalmology Clinics Using an EHR-Derived AI Algorithm.

PURPOSE: In patients with ophthalmic disorders, psychosocial risk factors play an important role in morbidity and mortality. Proper and early psychiatric screening can result in prompt intervention and mitigate its impact. Because screening is resource intensive, we developed a framework for automating screening using an electronic health record (EHR)-derived artificial intelligence (AI) algorithm...

Robotic, self-propelled, self-steerable, and disposable colonoscopes: Reality or pipe dream? A state of the art review.

Robotic colonoscopes could potentially provide a comfortable, less painful and safer alternative to standard colonoscopy. Recent exciting developments...

Machine Learning and Artificial Intelligence in Toxicological Sciences.

Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes represe...

Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components.

Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown gr...

A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms.

Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on the Breast Imaging Reporting and Data Syste...

Bilateral Analysis Boosts the Performance of Mammography-based Deep Learning Models in Breast Cancer Risk Prediction.

Breast cancer is one of the leading causes of death among women. Early prediction of breast cancer can significantly improve the survival rates. Breas...

Deep learning methods for lesion detection on mammography images: a comparative analysis.

Automatic lesion segmentation in mammography images assists in the diagnosis of breast cancer, which is the most common type of cancer especially amon...

Extravasation Screening and Severity Prediction from Skin Lesion Image using Deep Neural Networks.

Extravasation occurs secondary to the leakage of medication from blood vessels into the surrounding tissue during intravenous administration resulting...

Natural Language Processing to Identify Abnormal Breast, Lung, and Cervical Cancer Screening Test Results from Unstructured Reports to Support Timely Follow-up.

Cancer screening and timely follow-up of abnormal results can reduce mortality. One barrier to follow-up is the failure to identify abnormal results. ...

A Machine Learning-Based Predictive Model to Identify Patients Who Failed to Attend a Follow-up Visit for Diabetes Care After Recommendations From a National Screening Program.

OBJECTIVE: Reportedly, two-thirds of the patients who were positive for diabetes during screening failed to attend a follow-up visit for diabetes care...

D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19.

Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit d...

Systematic TB screening using WHO radiograph categorisation and care outcomes.

An appropriate screening approach and quality care are crucial for TB programmes in prisons. This study assessed crude TB prevalence, accuracy of the...

Identification of colorectal cancer using structured and free text clinical data.

Colorectal cancer incidence has continually fallen among those 50 years old and over. However, the incidence has increased in those under 50. Even wit...

Vaccine Design by Reverse Vaccinology and Machine Learning.

Reverse vaccinology (RV) is the state-of-the-art vaccine development strategy that starts with predicting vaccine antigens by bioinformatics analysis ...

Artificial Intelligence in Vaccine and Drug Design.

Knowledge in the fields of biochemistry, structural biology, immunological principles, microbiology, and genomics has all increased dramatically in re...

Artificial Intelligence for Vaccine Design.

Often likened to "the new electricity," artificial intelligence (AI) has broad and sweeping impact in many areas. Perhaps most exciting among these ar...

Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning.

PURPOSE: In this paper, we propose deep-learning methodology with which to enhance the mass differentiation performance of convolutional neural networ...

Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis.

To evaluate the performance of an artificial intelligence (AI) algorithm in a simulated screening setting and its effectiveness in detecting missed a...

Health Promotion, Health Literacy and Vaccine Hesitancy: The Role of Humanoid Robots.

The use of humanoid robot technologies within global healthcare settings is rapidly evolving; however, the potential of robots in health promotion and...