Unlabelled: Radiological diagnostic errors are common and may have severe consequences. Understanding these errors and their possible causes is crucial for optimising patient care and improving radiological training. Recent postmortem studies using an animal model highlighted the difficulties associated with accurate fracture diagnosis using radiological imaging. The present study aimed to highlight the fact that certain fractures are easily missed on CT scans in a clinical setting and that caution is advised. A few such cases were discussed to raise the level of suspicion to prevent similar diagnostic errors in future cases. Records of adult patients from the radiological department at an academic hospital in South Africa were retrospectively reviewed. Case studies were selected by identifying records of patients between January and June 2021 where traumatic fractures were missed during initial imaging interpretation but later detected during secondary analysis or on follow-up scans. Seven cases were identified, and the possible causes of the diagnostic errors were evaluated by reviewing the history of each case, level of experience of each reporting radiologist, scan quality and time of day that initial imaging interpretation of each scan was performed. The causes were multifactorial, potentially including a lack of experience, fatigue, heavy workloads or inadequate training of the initial reporting radiologist. Identifying these causes, openly discussing them and providing additional training for radiologists may aid in reducing these errors.
Contribution: This article aimed to use case examples of missed injuries on CT scanning of patients in a South African emergency trauma setting in order to highlight and provide insight into common errors in scan interpretation, their causes and possible means of mitigating them.
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http://dx.doi.org/10.4102/sajr.v26i1.2516 | DOI Listing |
JMIR Cancer
January 2025
Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology reports, facilitating the interpretation of textual content, and enhancing data utility through clustering techniques. Clustering allows for the identification of similar lesions and disease patterns across a broad dataset, making it useful for aggregating information and discovering new insights in medical imaging.
View Article and Find Full Text PDFSpectrochim Acta A Mol Biomol Spectrosc
January 2025
Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Spain. Electronic address:
The presence of cells in urine and in particular White Blood Cells (WBCs) is often associated with Urinary Tract Infections (UTIs) and other diseases. Non-invasive screening of WBCs requires the development of cost-effective point of care diagnostic tools. Infrared (IR) spectroscopy has the potential to identify and quantify cells in urine.
View Article and Find Full Text PDFInt J Surg Case Rep
January 2025
Department of Obstetrics and Gynecology, Moriya Daiichi General Hospital, Moriya, Ibaraki, Japan.
Introduction And Importance: Fallopian tube cancer, particularly the carcinosarcoma subtype, is a rare malignancy posing diagnostic challenges.
Case Presentation: Our patient was an 83-year-old, nulligravida woman, presented to our outpatient clinic with one month of pelvic pain. On examination, a pelvic mass was detected.
Sensors (Basel)
January 2025
Smart Diagnostic and Online Monitoring, Leipzig University of Applied Sciences, Wächterstraße 13, 04107 Leipzig, Germany.
This paper presents a comparative study of different AI models for indoor positioning systems, emphasizing improvements in localization accuracy and processing time. This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman filter using a real Received Signal Strength Indicator (RSSI) and 9-axis ICM-20948 sensor. An in-depth analysis is provided in this paper for data cleaning and feature selection to reduce errors for all the models.
View Article and Find Full Text PDFJ Clin Med
January 2025
Innovation, Research and Teaching Service (SABES-ASDAA), Teaching Hospital of the Paracelsus Medical Private University (PMU), 39100 Bolzano, Italy.
: This study investigates the potential of artificial intelligence (AI), specifically large language models (LLMs) like ChatGPT, to enhance decision support in diagnosing epilepsy. AI tools can improve diagnostic accuracy, efficiency, and decision-making speed. The aim of this study was to compare the level of agreement in epilepsy diagnosis between human experts (epileptologists) and AI (ChatGPT), using the 2014 International League Against Epilepsy (ILAE) criteria, and to identify potential predictors of diagnostic errors made by ChatGPT.
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