Preventive Care

Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy.

BACKGROUND/AIM: To study the impact of computer-aided detection (CADe) system on the detection rate of polyps and adenomas in colonoscopy.

Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram.

For computer-aided diagnosis (CAD), detection, segmentation, and classification from medical imagery are three key components to efficiently assist physicians for accurate diagnosis. In this chapter, a completely integrated CAD system based on deep learning is presented to diagnose breast lesions from digital X-ray mammograms involving detection, segmentation, and classification. To automatically ...

Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis.

Early detection of glaucoma is important to slow down progression of the disease and to prevent total vision loss. Retinal fundus photography is frequ...

Positive Predictive Value Surfaces as a Complementary Tool to Assess the Performance of Virtual Screening Methods.

BACKGROUND: Since their introduction in the virtual screening field, Receiver Operating Characteristic (ROC) curve-derived metrics have been widely us...

[New Trends in Breast Imaging].

New Trends in Breast Imaging The examination of the breast, especially as a screening examination for breast cancer, has so far been carried out prim...

A Survey on Machine Learning Algorithms for the Diagnosis of Breast Masses with Mammograms.

Breast cancer is leading cancer among women for the past 60 years. There are no effective mechanisms for completely preventing breast cancer. Rather i...

Investigation of Low-Dose CT Lung Cancer Screening Scan "Over-Range" Issue Using Machine Learning Methods.

Low-dose computed tomography (CT) lung cancer screening is recommended by the US Preventive Services Task Force for high lung cancer-risk populations....

[Rectal Gastrointestinal Stromal Tumor(GIST)Excised by Two Teams Following Neoadjuvant Chemotherapy-A Case Report].

A 67-year-old man presented with bloody stools. Colonoscopy showed a small submucosal tumor in the lower rectum. As the tumor was small, follow-up was...

Comprehensive Word-Level Classification of Screening Mammography Reports Using a Neural Network Sequence Labeling Approach.

Radiology reports contain a large amount of potentially valuable unstructured data. Recently, neural networks have been employed to perform classifica...

An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.

BACKGROUND: Human papillomavirus vaccination and cervical screening are lacking in most lower resource settings, where approximately 80% of more than ...

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers.

Recent advances in artificial intelligence (AI) are providing an opportunity to enhance existing clinical decision support (CDS) tools to improve pati...

Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm.

Radiological measurements are reported in free text reports, and it is challenging to extract such measures for treatment planning such as lesion summ...

Ontologic Model of Diagnostics and Treatment of Gastrointestinal Bleedings of Unknown Origin.

The article presents the semantic model of diagnostics and treatment of patients with gastrointestinal bleedings when the reasons of bleeding cannot b...

Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning.

Glaucoma is the second leading cause of blindness worldwide. This paper proposes an automated glaucoma screening method using retinal fundus images vi...

Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health.

Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process th...

A Machine Learning Strategy for Autism Screening in Toddlers.

OBJECTIVE: Autism spectrum disorder (ASD) screening can improve prognosis via early diagnosis and intervention, but lack of time and training can dete...

Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers.

Applying state-of-the-art machine learning techniques to medical images requires a thorough selection and normalization of input data. One of such ste...

Emerging next-generation robotic colonoscopy systems towards painless colonoscopy.

Advances in the field of robotics have allowed modern technology to be integrated into medicine and that can minimize patients suffering from the side...

Overview of Deep Learning in Gastrointestinal Endoscopy.

Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine i...

Accurate Identification of Colonoscopy Quality and Polyp Findings Using Natural Language Processing.

OBJECTIVES: The aim of this study was to test the ability of a commercially available natural language processing (NLP) tool to accurately extract exa...