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  • Author: Daniela Dobru x
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Open access

Gabriella Gábos, Dumitru Moldovan and Daniela Dobru

Abstract

Hereditary angioedema (HAE) caused by a deficiency of C1 esterase inhibitor enzyme (C1-INH) is a very rare, autosomal dominantly inherited genetic disorder, characterized by recurrent peripheral angioedema, painful abdominal attacks and episodes of laryngeal edema. Abdominal attacks are frequent symptoms in adult HAE patients, occurring in more than 90% of the cases. Angioedema in the bowel or abdomen can occur in the absence of cutaneous manifestations and may be easily misdiagnosed unless the clinician has a high degree of awareness to include HAE in the differential diagnosis. Misdiagnosis is associated with inadequate treatments, including unnecessary surgical procedures. Any patient who presents recurrent episodes of swelling should be evaluated for HAE caused by C1-INH deficiency. New therapies could save lives and dramatically improve their quality of life.

Open access

Andrei-Constantin Ioanovici, Andrei-Marian Feier, Ioan Țilea and Daniela Dobru

Abstract

Colorectal cancer is an important health issue, both in terms of the number of people affected and the associated costs. Colonoscopy is an important screening method that has a positive impact on the survival of patients with colorectal cancer. The association of colonoscopy with computer-aided diagnostic tools is currently under researchers’ focus, as various methods have already been proposed and show great potential for a better management of this disease. We performed a review of the literature and present a series of aspects, such as the basics of machine learning algorithms, different computational models as well as their benchmarks expressed through measurements such as positive prediction value and accuracy of detection, and the classification of colorectal polyps. Introducing computer-aided diagnostic tools can help clinicians obtain results with a high degree of confidence when performing colonoscopies. The growing field of machine learning in medicine will have a big impact on patient management in the future.