Publications by authors named "Lotte Ewals"
The early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for optimal treatment of pancreatic cancer patients. We propose a tumor detection framework to improve the detection of pancreatic head tumors on CT scans. In this retrospective research study, CT images of 99 patients with pancreatic head cancer and 98 control cases from the Catharina Hospital Eindhoven were collected.
View Article and Find Full Text PDFBackground: Many promising artificial intelligence (AI) and computer-aided detection and diagnosis systems have been developed, but few have been successfully integrated into clinical practice. This is partially owing to a lack of user-centered design of AI-based computer-aided detection or diagnosis (AI-CAD) systems.
Objective: We aimed to assess the impact of different onboarding tutorials and levels of AI model explainability on radiologists' trust in AI and the use of AI recommendations in lung nodule assessment on computed tomography (CT) scans.
Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy.
View Article and Find Full Text PDFArticle Synopsis
- AI algorithms have been developed to help radiologists reduce missed or misdiagnosed lung nodules on CT scans, with some already in practice.
- Studies show that AI assistance generally improves radiologists' sensitivity and overall performance in detecting lung nodules and predicting malignancy, although specificity may be slightly lower.
- Despite promising results, there's a need for more research on the practical integration of AI tools in clinical workflows, their clinical validation, and impacts on patient follow-up.