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33 active trials for Iron Deficiency Anemia

Oral Iron Versus Oral Iron Plus a Web-based Behavioral Intervention in Young Children (IRONCHILD)

Iron deficiency anemia (IDA) affects nearly half a million young children in the United States. Most children take liquid iron medicine by mouth for at least 3 months. However, some children take longer to get better with this medicine. This study is trying to compare different ways of giving iron medicine to young children. For young children in the US, the main cause of IDA is nutritional, or not having enough iron in the foods they eat. This often happens when kids drink too much cow milk and/or not eating enough foods that have a lot of iron. Iron deficiency is most common in children ages 1 to 4 years of age, during a time that is important for brain development. More severe and long-lasting IDA is associated with worse brain development outcomes. That is why researchers want to understand the fastest way for kids with IDA to get better. Standard treatment is oral iron medicine for 3 to 6 months. Many children do not take their iron medicine the full amount of time needed because of side effects like abdominal discomfort, nausea, constipation, and bad taste. Different factors can contribute to patients not completing their IDA therapy. Many families do not understand how important it is to treat IDA or do not have the motivation to continue the medication. This study will offer different methods for treating IDA, including a different method to taking the oral iron therapy. This new method gives oral iron by increasing a family's understanding and motivation. Another research study that interviewed families of young children with IDA found ways that helped the patients to continue their therapy. Using that information, a website called IRONCHILD was created to help motivate parents to get their children to continue the oral iron medicine. Research studies that compare these different IDA treatment methods in young children are needed and could have benefits to short-term clinical and long-term brain development. However, we do not know whether families of young children with IDA will be willing to participate in this type of study that has different treatment methods (oral iron therapy and oral iron therapy with a web-based adherence intervention). The goal of this clinical research study is to learn which of the two methods of care will be the best way for children with iron deficiency anemia to receive therapy.

Start: May 2021
Role of AI in CE for the Identification of SB Lesions in Patients With Small Intestinal Bleeding.

Capsule Endoscopy (CE) is a safe, patient friendly and easy procedure performed for the evaluation of gastrointestinal tract unable to be explored via conventional endoscopy. The most common indication to perform SBCE is represented by Suspected Small Bowel Bleeding (SSBB). According to the widest meta-analysis available in literature, SBCE shows a diagnostic yield in SSBB of about 60%, and angiodysplasias are the most relevant findings, accounting for 50% of patients undergoing SBCE for SSBB. Accordingly, it represents the first line examination in SSBB investigation for determining the source of bleeding, if primary endoscopy results negative. Despite its high clinical feasibility, the evaluation of CE-video-captures is one of the main drawbacks since it is time consuming and requests the reader to concentrate to not miss any lesion. In order to reduce reading time, several software have been developed with the aim to cut similar images and select relevant images. For example, automated fast reading software have demonstrated to significantly reduce reading time without impacting the miss rate in pathological conditions affecting diffusely the mucosa (as IBD lesions do). Not the same assumption can be taken for isolated lesions since several studies reported an unacceptable miss rate for such a detection modality. New advancements such as artificial intelligence made their appearance in recent years. Deep convolutional neural networks (CNNs) have demonstrated to recognize specific images among a large variety up to exceed human performance in visual tasks. A Deep Learning model has been recently validated in the field of Small Bowel CE by Ding et al. According to their data collected on 5000 patients, the CNN-based auxiliary model identify abnormalities with 99.88% sensitivity in the per patient analysis and 99.90% sensitivity in the per-lesion analysis. With this perspective, it is believable that AI applied to SBCE can significantly shorten the reading time and support physicians to detect available lesions without losing significant lesions, further improving the diagnostic yield of the procedure.

Start: February 2021