{"id":11677,"date":"2025-04-04T09:36:23","date_gmt":"2025-04-04T07:36:23","guid":{"rendered":"https:\/\/edihadria.eu\/?p=11677"},"modified":"2025-04-04T12:10:57","modified_gmt":"2025-04-04T10:10:57","slug":"digitalna-transformacija-u-zdravstvu-primjena-umjetne-inteligencije-u-ocitanju-mamografskih-nalaza-u-domu-zdravlja-primorsko-goranske-zupanije","status":"publish","type":"post","link":"https:\/\/edihadria.eu\/en\/digitalna-transformacija-u-zdravstvu-primjena-umjetne-inteligencije-u-ocitanju-mamografskih-nalaza-u-domu-zdravlja-primorsko-goranske-zupanije\/","title":{"rendered":"Digital transformation in health: Application of artificial intelligence in the reading of mammographic findings in the Health Center of Primorje-Gorski Kotar County"},"content":{"rendered":"

Challenges<\/strong><\/h3>\n

The health center of Primorje-Gorski Kotar County, as well as many other health institutions in Croatia and beyond, faces long and resource-intensive processes of reading mammograms. One of the key challenges is the chronic lack of radiologists, resulting in long waiting lists, delayed diagnosis, and an increased risk of missing early signs of breast cancer.<\/p>\n

In the context of early detection of breast cancer, time is a key factor. Each day of delay in reading can mean the difference between a successful treatment and a late, more complicated intervention. In addition, the interpretation of mammograms often involves a certain dose of subjectivity, which further increases the pressure on the limited number of available radiologists and reduces diagnostic consistency among specialists.<\/p>\n

Due to all of the above, it has become necessary to consider the use of artificial intelligence (AI) as a technological partner in addressing these challenges. AI would not replace professionals, but allow them to do their job more efficiently, quickly and safely.<\/p>\n

Solution<\/strong><\/h3>\n

As part of the Test Before Invest (TBI) activities, EDIH Adria proposed and tested the application of an AI model for automatic detection and localisation of lesions on mammograms, based on the YOLOv11 algorithm \u2013 one of the most advanced and fastest computer vision models for real-time image analysis.<\/p>\n

Detection and localisation of suspected lesions - YOLOv11<\/strong><\/h4>\n

The YOLOv11 (You Only Look Once) model uses deep convolutional neural networks to analyze the entire image in one pass, allowing very fast and precise detection of suspicious masses. The system detects and classifies lesions as potentially benign or malignant and automatically indicates their positions in the mammogram picture, along with the level of confidence for each finding.<\/p>\n

During testing, the model showed significant results in terms of speed and accuracy, and an additional advantage is that it can be adjusted and additionally trained on domestic data, which would increase the specificity in the context of the Croatian health system.<\/p>\n

The system is conceived as a tool that supports patient triage \u2013 it indicates risky cases and allows for their priority examination, thus significantly contributing to the optimization of the diagnostic process in conditions of limited resources.<\/p>\n