AIMC Topic: Sensitivity and Specificity

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Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients wi...

Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis.

Journal of neurointerventional surgery
BACKGROUND: This study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH).

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

Scientific reports
Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-b...

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis.

Journal of neurointerventional surgery
BACKGROUND: Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided by automated systems, may improve patient outcomes. Therefore, a systematic review and meta-analysis ...

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.