AIMC Topic: Biopsy, Fine-Needle

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A Preliminary Study Comparing the Performance of Thyroid Molecular Tests to a Deep Learning Algorithm in Predicting Malignancy in Indeterminate Thyroid Fine Needle Aspiration Biopsies.

Thyroid : official journal of the American Thyroid Association Mar 4, 2024
Deep Learning Thyroid Neoplasms Thyroid Nodule Biopsy, Fine-Needle Humans Retrospective Studies
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Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier.

Scientific reports Aug 19, 2023
Fine needle aspiration (FNA) biopsy of thyroid nodules is a safe, cost-effective, and accurate diagnostic method for detecting thyroid cancer. However, about 10% of initial FNA biopsy samples from patients are non-diagnostic and require repeated FNA,...
Deep Learning Thyroid Gland Biopsy, Fine-Needle Glass Mental Recall Humans
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Radiologists with assistance of deep learning can achieve overall accuracy of benign-malignant differentiation of musculoskeletal tumors comparable with that of pre-surgical biopsies in the literature.

International journal of computer assisted radiology and surgery Jan 18, 2023
PURPOSE: The purpose of this study was to assess if radiologists assisted by deep learning (DL) algorithms can achieve diagnostic accuracy comparable to that of pre-surgical biopsies in benign-malignant differentiation of musculoskeletal tumors (MST)...
Biopsy, Fine-Needle Biopsy, Large-Core Needle Humans Retrospective Studies Radiologists Datasets as Topic Deep Learning Systematic Reviews as Topic
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Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network.

Scientific reports Oct 8, 2021
To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined s...
Humans Female Male Middle Aged Thyroid Neoplasms Neural Networks, Computer Prognosis Retrospective Studies Adenocarcinoma, Follicular Cytological Techniques Cytodiagnosis Ultrasonography Observer Variation Thyroid Nodule Biopsy, Fine-Needle Follow-Up Studies
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Comparison of British Thyroid Association, American College of Radiology TIRADS and Artificial Intelligence TIRADS with histological correlation: diagnostic performance for predicting thyroid malignancy and unnecessary fine needle aspiration rate.

The British journal of radiology Jun 9, 2021
OBJECTIVES: To compare diagnostic performance of British Thyroid Association (BTA), American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) and Artificial Intelligence TIRADS (AI-TIRADS) for thyroid nodule malignancy. To ...
Practice Guidelines as Topic Biopsy, Fine-Needle Unnecessary Procedures Ultrasonography Predictive Value of Tests Neoplasm Grading Adult Sensitivity and Specificity Male Retrospective Studies Thyroidectomy Female Thyroid Neoplasms Aged Middle Aged Aged, 80 and over Adolescent Humans Artificial Intelligence
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Applications of Computational Pathology in Head and Neck Cytopathology.

Acta cytologica Feb 23, 2021
BACKGROUND: The application of computational technology to head and neck cytology material has been explored experimentally in several areas with a variety of potential applications.
Salivary Gland Neoplasms Reproducibility of Results Diagnosis, Computer-Assisted Thyroid Neoplasms Humans Artificial Intelligence Image Interpretation, Computer-Assisted Squamous Cell Carcinoma of Head and Neck Biopsy, Fine-Needle Predictive Value of Tests
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Artificial Intelligence in Thyroid Fine Needle Aspiration Biopsies.

Acta cytologica Dec 16, 2020
BACKGROUND: From cell phones to aerospace, artificial intelligence (AI) has wide-reaching influence in the modern age. In this review, we discuss the application of AI solutions to an equally ubiquitous problem in cytopathology - thyroid fine needle ...
Humans Reproducibility of Results Diagnosis, Computer-Assisted Neural Networks, Computer Machine Learning Thyroid Neoplasms Diagnosis, Differential Predictive Value of Tests Image Interpretation, Computer-Assisted Biopsy, Fine-Needle
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A Comparative Analysis of Two Machine Learning-Based Diagnostic Patterns with Thyroid Imaging Reporting and Data System for Thyroid Nodules: Diagnostic Performance and Unnecessary Biopsy Rate.

Thyroid : official journal of the American Thyroid Association Sep 9, 2020
The risk stratification system of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for thyroid nodules is affected by low diagnostic specificity. Machine learning (ML) methods can optimize the diagnostic perf...
Unnecessary Procedures Clinical Decision-Making Elasticity Imaging Techniques Predictive Value of Tests Female Tumor Burden Reproducibility of Results Thyroid Neoplasms Male Aged, 80 and over China Image Interpretation, Computer-Assisted Humans Aged Middle Aged Ultrasonography Young Adult Adolescent Biopsy, Fine-Needle Machine Learning Thyroid Nodule Adult Retrospective Studies
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Review of different platforms to perform rapid onsite evaluation via telecytology.

Cytopathology : official journal of the British Society for Clinical Cytology Jul 5, 2020
There is increased utilisation of cytopathology to provide a rapid onsite evaluation (ROSE) of fine needle aspiration and touch preparations of small biopsies. A well-executed ROSE procedure can significantly impact the diagnostic quality and appropr...
Humans Robotics Remote Consultation Biopsy, Fine-Needle Cytodiagnosis Telemedicine
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Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta-analysis.

Diagnostic cytopathology Jun 12, 2020
BACKGROUND: The role of retrospective analysis has been evolved greatly in cancer research. We undertook this meta-analysis to evaluate the diagnostic value of Neural networks (NNs) in Fine needle aspiration cytological (FNAC) image of cancer.
Biopsy, Fine-Needle Humans Sensitivity and Specificity Algorithms Neoplasms Retrospective Studies Deep Learning
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