New achievements for SenticLab in lung cancer recognition

The Senticlab team has developed a new solution based on artificial intelligence to automatically recognize tumoral nodules from chest radiographs, obtaining one of the best results in the NODE21 competition endorsed by the MICCAI Society.

Medical Imaging Analysis and Diagnostics Machine Learning Artificial Intelligence

Lung cancer is a serious illness causing the greatest number of cancer deaths worldwide. Symptoms of lung cancer typically occur at an advanced stage of the disease, when treatment has a reduced chance of success. For these reasons, early diagnosis is crucial in order to reduce mortality rates from lung cancer.

To this end, screening processes are based on the identification of pulmonary nodules, which can be detected by analyzing chest radiograph and are visible well before clinical symptoms or signs emerge. Since exams requiring chest radiographs are quite common, pulmonary nodules are frequently encountered as incidental findings in patients undergoing routine examination or CXR imaging for issues unrelated to lung cancer.

This context has always been of particular interest for the product development roadmap within SenticLab, which includes AI-assisted diagnosis software for lung pathologies.

Polmonar nodes detection

Senticlab has developed a solution based on YOLO, a modern computer vision algorithm which is especially effective for object detection tasks. By applying a properly modified version of YOLO on chest radiographs, it is possible to identify tumoral nodules with an accuracy of 82.75%. This result, based on a dataset of 4882 frontal chest radiographs, allowed Senticlab to be ranked second among all the other solutions participating in NODE21 for the detection task, just over a percentage point from the winning solution (MTEC, Hamburg University of Technology), and distancing the solution proposed by the prestigious UCLA by two percentage points.

Beyond the results, this is the umpteenth proof of the effectiveness of AI in supporting medical diagnosis. In close collaboration with synbrAIn, Senticlab has developed AI modules included in several applications that have already been placed on the market within the MS HUMANAID platform for the automatic identification of pneumonia from medical images. In addition to the identification of pulmonary nodules, Senticlab and synbrAIn are working to extend the functionality of MS HUMANAID / AIRX with CT analysis and the identification of Pneumothorax.