AIMC Topic: Sensitivity and Specificity

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Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.

European radiology
OBJECTIVES: Prostate volume (PV) in combination with prostate specific antigen (PSA) yields PSA density which is an increasingly important biomarker. Calculating PV from MRI is a time-consuming, radiologist-dependent task. The aim of this study was t...

Automated Detection of Cerebral Aneurysms on TOF-MRA Using a Deep Learning Approach: An External Validation Study.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Cerebral aneurysms yield the risk of rupture, severe disability and death. Thus, early detection of cerebral aneurysms is crucial to ensure timely treatment, if necessary. AI-based software tools are expected to enhance radiol...

Ferrobotic swarms enable accessible and adaptable automated viral testing.

Nature
Expanding our global testing capacity is critical to preventing and containing pandemics. Accordingly, accessible and adaptable automated platforms that in decentralized settings perform nucleic acid amplification tests resource-efficiently are requi...

End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to develop a semi-supervised segmentation and classification deep learning model for the diagnosis of anterior cruciate ligament (ACL) tears on MRI based on a semi-supervised framework, double-linear layers U-Ne...

Prototype early diagnostic model for invasive pulmonary aspergillosis based on deep learning and big data training.

Mycoses
BACKGROUND: Currently, the diagnosis of invasive pulmonary aspergillosis (IPA) mainly depends on the integration of clinical, radiological and microbiological data. Artificial intelligence (AI) has shown great advantages in dealing with data-rich bio...

Accuracy of artificial intelligence for tooth extraction decision-making in orthodontics: a systematic review and meta-analysis.

Clinical oral investigations
OBJECTIVE: This study aimed to analyze the accuracy of artificial intelligence (AI) for orthodontic tooth extraction decision-making.

Deep learning-based imaging reconstruction for MRI after neoadjuvant chemoradiotherapy for rectal cancer: effects on image quality and assessment of treatment response.

Abdominal radiology (New York)
PURPOSE: To investigate the effects of deep learning-based imaging reconstruction (DLR) on the image quality of MRI of rectal cancer after chemoradiotherapy (CRT), and its accuracy in diagnosing pathological complete responses (pCR).

Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial.

Investigative radiology
OBJECTIVES: Computer-aided triaging (CAT) and computer-aided diagnosis (CAD) of screening breast magnetic resonance imaging have shown potential to reduce the workload of radiologists in the context of dismissing normal breast scans and dismissing be...

Automatic identification of benign pigmented skin lesions from clinical images using deep convolutional neural network.

BMC biotechnology
OBJECTIVE: We aimed to develop a computer-aided detection (CAD) system for accurate identification of benign pigmented skin lesions (PSLs) from images captured using a digital camera or a smart phone.

Detection of Proximal Caries Lesions on Bitewing Radiographs Using Deep Learning Method.

Caries research
This study aimed to evaluate the validity of a deep learning-based convolutional neural network (CNN) for detecting proximal caries lesions on bitewing radiographs. A total of 978 bitewing radiographs, 10,899 proximal surfaces, were evaluated by two ...