Publications by authors named "J F Bosma"
Background: Acute abdominal pain (AAP) constitutes 5-10% of all emergency department (ED) visits, with appendicitis being a prevalent AAP etiology often necessitating surgical intervention. The variability in AAP symptoms and causes, combined with the challenge of identifying appendicitis, complicate timely intervention. To estimate the risk of appendicitis, scoring systems such as the Alvarado score have been developed.
View Article and Find Full Text PDFLactic acidosis is a rare metabolic complication that can occur in patients with diabetes mellitus type 2 who use metformin. We discuss a 79-year old woman with metformin-associated lactic acidosis (MALA) and acute kidney injury based on gastroenteritis. Patient reported acute blindness which in literature is described as a rare presentation of a metabolic acidosis (regardless of its underlying cause).
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- Biparametric MRI (bpMRI) may serve as a valid alternative to multiparametric MRI (mpMRI) for diagnosing clinically significant prostate cancer (csPCa), as assessed in a large international observer study.
- The study involved 400 mpMRI examinations from four different European centers, where readers evaluated both bpMRI and mpMRI for their ability to accurately diagnose csPCa, finding them to be similarly effective.
- Key findings indicated that bpMRI and mpMRI had comparable diagnostic accuracy (AUROC values) and sensitivity, with bpMRI showing a noninferior performance, though both methods had similar specificity when distinguishing csPCa.
Article Synopsis
- This study investigates the potential for improving the diagnostic accuracy of detecting clinically significant prostate cancer (csPCa) on MRI by incorporating clinical parameters like prostate-specific antigen, prostate volume, and age into deep learning models.
- A total of 932 biparametric MRI exams were analyzed, and various AI models were tested, combining MRI-based deep learning results with the clinical parameters through different methods of data fusion.
- The results showed that the best model, which combined deep learning suspicion levels with clinical features, outperformed other models and had performance comparable to radiologist assessments in identifying csPCa.
Background: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.
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