Publications by authors named "L Laborie"
Article Synopsis
- Developmental dysplasia of the hip (DDH) affects about 2-3% of newborns and is linked to increased risks of osteoarthritis in young adults, leading to many hip replacements before age 40.
- The study reviewed 73 papers, including 31 focused on single nucleotide polymorphisms (SNPs) in DDH, but found many studies lacked sufficient power to identify significant genetic associations.
- While DDH is known to be hereditary, the research is limited and no definitive genetic risk factors have been identified, indicating a need for further investigations.
Article Synopsis
- Researchers wanted to understand how often young kids under two get fractures and if they’re from accidents or abuse.
- They looked at various studies from 1946 to 2024 and found that about 5 to 9 kids out of every 1,000 get fractures each year.
- The most common fractures happened in the arm and leg bones, while infants had fewer fractures, mostly in the collarbone.
Objectives: To examine radiologic outcomes at skeletal maturity of sonographically normal, immature, mildly, and severely dysplastic newborn hips.
Methods: During 1988 to 1990, 11 925 newborns were enrolled in a randomized controlled trial examining screening strategies for developmental hip dysplasia. In total, 4469 were invited to clinical and radiologic follow-up 18 years later, of which 1735 had received neonatal ultrasound.
Article Synopsis
- Developmental dysplasia of the hip (DDH) affects 2-3% of infants and is linked to increased risk of osteoarthritis and hip surgeries in younger adults, prompting this study to explore its genetic factors for better diagnosis and management.
- The research utilized data from the Trøndelag Health Study, analyzing over 69,000 individuals and over 8.5 million SNPs to identify genetic associations, specifically focusing on the SNPs rs713162 and rs6577334 near the COL11A1 gene, which has ties to hip disorders.
- Findings suggest a significant link between COL11A1 and DDH, supporting previous research on genetic influences and highlighting the need for further investigations into the condition's causes.
Over the past decade, there has been a dramatic rise in the interest relating to the application of artificial intelligence (AI) in radiology. Originally only 'narrow' AI tasks were possible; however, with increasing availability of data, teamed with ease of access to powerful computer processing capabilities, we are becoming more able to generate complex and nuanced prediction models and elaborate solutions for healthcare. Nevertheless, these AI models are not without their failings, and sometimes the intended use for these solutions may not lead to predictable impacts for patients, society or those working within the healthcare profession.
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