Background: The ultrasonographic estimation of thyroid size has been advocated as being more precise than palpation to diagnose goitre. However, ultrasound also requires technical proficiency. This study was conducted among Saharawi refugees, where goitre is highly prevalent. The objectives were to assess the overall data quality of ultrasound measurements of thyroid volume (Tvol), including the intra- and inter-observer agreement, under field conditions, and to describe some of the practical challenges encountered.
Methods: In 2007 a cross-sectional study of 419 children (6-14 years old) and 405 women (15-45 years old) was performed on a population of Saharawi refugees with prevalent goitre, who reside in the Algerian desert. Tvol was measured by two trained fieldworkers using portable ultrasound equipment (examiner 1 measured 406 individuals, and examiner 2, 418 individuals). Intra- and inter-observer agreement was estimated in 12 children selected from the study population but not part of the main study. In the main study, an observer error was found in one examiner whose ultrasound images were corrected by linear regression after printing and remeasuring a sample of 272 images.
Results: The intra-observer agreement in Tvol was higher in examiner 1, with an intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.91, 0.99) compared to 0.86 (95% CI: 0.60, 0.96) in examiner 2. The ICC for inter-observer agreement in Tvol was 0.38 (95% CI: -0.20, 0.77). Linear regression coefficients indicated a significant scaling bias in the original measurements of the AP and ML diameter and a systematic underestimation of Tvol (a product of AP, ML, CC and a constant). The agreement between re-measured and original Tvol measured by ICC (95% CI) was 0.76 (0.71, 0.81). The agreement between re-measured and corrected Tvol measured by ICC (95% CI) was 0.97 (0.96, 0.97).
Conclusions: An important challenge when using ultrasound to assess thyroid volume under field conditions is to recruit and train qualified personnel to perform the measurements. Methodological studies are important to assess data quality and can facilitate statistical corrections and improved estimates.
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http://dx.doi.org/10.1186/1475-2891-9-66 | DOI Listing |
Biomed Phys Eng Express
January 2025
National School of Electronics and Telecommunication of Sfax, Sfax rte mahdia, sfax, sfax, 3012, TUNISIA.
Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the impact of various data augmentation strategies on enhancing the performance of a customized convolutional neural network model for corneal topographic map classification.
View Article and Find Full Text PDFJ Particip Med
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Division of Allergy & Pulmonary Medicine, Washington University School of Medicine, St Louis, MO, United States.
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The Canadian Genomics Research and Development Initiative for Antimicrobial Resistance (GRDI-AMR) uses a genomics-based approach to understand how health care, food production and the environment contribute to the development of antimicrobial resistance. Integrating genomics contextual data streams across the One Health continuum is challenging because of the diversity in data scope, content and structure. To better enable data harmonization for analyses, a contextual data standard was developed.
View Article and Find Full Text PDFGac Med Mex
January 2025
Consultoría independiente, Mexico City, Mexico.
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Nutr Rev
January 2025
Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA 70808, United States.
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Data Sources: Searches were performed in the PubMed, Embase, Cochrane Central Register of Controlled Trials, and ProQuest Dissertations & Theses databases.
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