AIMC Topic: Sensitivity and Specificity
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The Potential Clinical Utility of an Artificial Intelligence Model for Identification of Vertebral Compression Fractures in Chest Radiographs.
Journal of the American College of Radiology : JACR
Sep 17, 2024
PURPOSE: To assess the ability of the Annalise Enterprise CXR Triage Trauma (Annalise AI Pty Ltd, Sydney, NSW, Australia) artificial intelligence model to identify vertebral compression fractures on chest radiographs and its potential to address undi...
Deep Learning Model for Pathological Grading and Prognostic Assessment of Lung Cancer Using CT Imaging: A Study on NLST and External Validation Cohorts.
Academic radiology
Sep 17, 2024
RATIONALE AND OBJECTIVES: To develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning.
The BCPM method: decoding breast cancer with machine learning.
BMC medical imaging
Sep 17, 2024
Breast cancer prediction and diagnosis are critical for timely and effective treatment, significantly impacting patient outcomes. Machine learning algorithms have become powerful tools for improving the prediction and diagnosis of breast cancer. The ...
Diagnostic accuracy of artificial intelligence-assisted caries detection: a clinical evaluation.
BMC oral health
Sep 16, 2024
OBJECTIVE: This clinical study aimed to evaluate the practical value of integrating an AI diagnostic model into clinical practice for caries detection using intraoral images.
Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.
Diagnostic and interventional imaging
Sep 14, 2024
PURPOSE: The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of autom...
Multimodal convolutional neural network-based algorithm for real-time detection and differentiation of malignant and inflammatory biliary strictures in cholangioscopy: a proof-of-concept study (with video).
Gastrointestinal endoscopy
Sep 13, 2024
BACKGROUND AND AIMS: Deep learning algorithms gained attention for detection (computer-aided detection [CADe]) of biliary tract cancer in digital single-operator cholangioscopy (dSOC). We developed a multimodal convolutional neural network (CNN) for ...
Middle Aged
Diagnosis, Computer-Assisted
Deep Learning
Convolutional Neural Networks
ROC Curve
Female
Aged
Sensitivity and Specificity
Male
Proof of Concept Study
Cholangiocarcinoma
Endoscopy, Digestive System
Biliary Tract Neoplasms
Cholestasis
Diagnosis, Differential
Constriction, Pathologic
Video Recording
Bile Duct Neoplasms
Humans
Neural Networks, Computer
Algorithms
Cholangitis
Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.
BMC medical imaging
Sep 13, 2024
BACKGROUND: To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma.
Applying deep learning-based ensemble model to [F]-FDG-PET-radiomic features for differentiating benign from malignant parotid gland diseases.
Japanese journal of radiology
Sep 10, 2024
OBJECTIVES: To develop and identify machine learning (ML) models using pretreatment 2-deoxy-2-[F]fluoro-D-glucose ([F]-FDG)-positron emission tomography (PET)-based radiomic features to differentiate benign from malignant parotid gland diseases (PGDs...
A Machine Learning-Driven Surface-Enhanced Raman Scattering Analysis Platform for the Label-Free Detection and Identification of Gastric Lesions.
International journal of nanomedicine
Sep 10, 2024
BACKGROUND: Gastric lesions pose significant clinical challenges due to their varying degrees of malignancy and difficulty in early diagnosis. Early and accurate detection of these lesions is crucial for effective treatment and improved patient outco...
Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.
Diagnostic and interventional radiology (Ankara, Turkey)
Sep 9, 2024
PURPOSE: This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdominopelvic computed tomography (CT) that were initially overlooked by radiologists.
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