AIMC Topic: Sensitivity and Specificity

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[Deep transfer learning radiomics model based on temporal bone CT for assisting in the diagnosis of inner ear malformations].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery
To evaluate the diagnostic efficacy of traditional radiomics, deep learning, and deep learning radiomics in differentiating normal and inner ear malformations on temporal bone computed tomography(CT). A total of 572 temporal bone CT data were retrosp...

Application of Computer-Assisted Endoscopic Ultrasonography Based on Texture Features in Differentiating Gastrointestinal Stromal Tumors from Benign Gastric Mesenchymal Tumors.

The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology
BACKGROUND/AIMS:  Gastrointestinal stromal tumors are common gastric mesenchymal tumors that are potentially malignant. However, endoscopic ultrasonography is poor in diagnosing gastrointestinal stromal tumors. The study investigated the efficacy of ...

Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermos...

Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis.

Neurosciences (Riyadh, Saudi Arabia)
OBJECTIVES: The brain and spinal cord, constituting the central nervous system (CNS), could be impacted by an inflammatory disease known as multiple sclerosis (MS). The convolutional neural networks (CNN), a machine learning method, can detect lesion...

Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time.

Radiology. Artificial intelligence
Purpose To develop an artificial intelligence (AI) model for the diagnosis of breast cancer on digital breast tomosynthesis (DBT) images and to investigate whether it could improve diagnostic accuracy and reduce radiologist reading time. Materials an...

Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan.

Radiology. Artificial intelligence
Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials and Methods An AI assistant for lung cancer screening was evaluated on two retrospective randomized m...

Diagnostic accuracy of artificial intelligence versus manual detection in marginal bone loss around fixed prosthesis. a systematic review.

JPMA. The Journal of the Pakistan Medical Association
OBJECTIVES: The aim of the review is to evaluate the existing precision of artificial intelligence (AI) in detecting Marginal Bone Loss (MBL) around prosthetic crowns using 2-Dimentional radiographs. It also summarises the recent advances and future ...

Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss.

Mathematical biosciences and engineering : MBE
Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imba...

Deep learning for acute rib fracture detection in CT data: a systematic review and meta-analysis.

The British journal of radiology
OBJECTIVES: To review studies on deep learning (DL) models for classification, detection, and segmentation of rib fractures in CT data, to determine their risk of bias (ROB), and to analyse the performance of acute rib fracture detection models.