Whether there is a precise relationship between reading speed and diagnostic accuracy has been an elusive and much debated issue. We discuss the literature and include practical considerations and relevant experience. To our knowledge, no credible relationship has been established between the speed of diagnostic image interpretation and accuracy. Furthermore, no nationally recognized guidelines address these factors, and it would be irresponsible to attribute widespread credibility to anecdotal studies. A variety of factors influence diagnostic accuracy, and length of interpretation time is not an established one.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.2214/AJR.19.21290 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Dynamic Features Driven by Stochastic Collisions in a Nanopore for Precise Single-Molecule Identification.
J Am Chem Soc
January 2025
Molecular Sensing and Imaging Center, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China.
Nanopore technology holds great potential for single-molecule identification. However, extracting meaningful features from ionic current signals and understanding the molecular mechanisms underlying the specific features remain unresolved. In this study, we uncovered a distinctive ionic current pattern in a K238Q aerolysin nanopore, characterized by transient spikes superimposed on two stable transition states.
View Article and Find Full Text PDFNMRformer: A Transformer-Based Deep Learning Framework for Peak Assignment in 1D H NMR Spectroscopy.
Anal Chem
January 2025
Institute of Artificial Intelligence, Xiamen University, Xiamen, Fujian, 361005, China.
Metabolite identification from 1D H NMR spectra is a major challenge in NMR-based metabolomics. This study introduces NMRformer, a Transformer-based deep learning framework for accurate peak assignment and metabolite identification in 1D H NMR spectroscopy. Unlike traditional approaches, NMRformer interprets spectra as sequences of spectral peaks and integrates a self-attention mechanism and peak height ratios directly into the Transformer encoder layer.
View Article and Find Full Text PDFRapid on-site evaluation using telecytology: Quality assurance study at a high-volume cancer center.
Cancer Cytopathol
January 2025
Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Background: Telecytology-assisted rapid on-site evaluation (ROSE) offers a cost-effective method to enhance minimally invasive biopsies like fine needle aspiration and core biopsies with touch preparation. By reducing nondiagnostic sampling and the need for repeat procedures, ROSE via telecytology facilitates prompt triage for ancillary tests, improving patient management. This study examines cases initially deemed adequate for diagnosis during telecytology-assisted ROSE but later categorized as nondiagnostic at final evaluation (NDIS).
View Article and Find Full Text PDFMachine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor.
J Chem Inf Model
January 2025
Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
Despite remarkable advancements in the organic synthesis field facilitated by the use of machine learning (ML) techniques, the prediction of reaction outcomes, including yield estimation, catalyst optimization, and mechanism identification, continues to pose a significant challenge. This challenge arises primarily from the lack of appropriate descriptors capable of retaining crucial molecular information for accurate prediction while also ensuring computational efficiency. This study presents a successful application of ML for predicting the performance of Ir-catalyzed allylic substitution reactions.
View Article and Find Full Text PDFMultimodal sleep staging network based on obstructive sleep apnea.
Front Comput Neurosci
December 2024
School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China.
Background: Automatic sleep staging is essential for assessing sleep quality and diagnosing sleep disorders. While previous research has achieved high classification performance, most current sleep staging networks have only been validated in healthy populations, ignoring the impact of Obstructive Sleep Apnea (OSA) on sleep stage classification. In addition, it remains challenging to effectively improve the fine-grained detection of polysomnography (PSG) and capture multi-scale transitions between sleep stages.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!