2 research outputs found
RCVaR: an Economic Approach to Estimate Cyberattacks Costs using Data from Industry Reports
Digitization increases business opportunities and the risk of companies being
victims of devastating cyberattacks. Therefore, managing risk exposure and
cybersecurity strategies is essential for digitized companies that want to
survive in competitive markets. However, understanding company-specific risks
and quantifying their associated costs is not trivial. Current approaches fail
to provide individualized and quantitative monetary estimations of
cybersecurity impacts. Due to limited resources and technical expertise, SMEs
and even large companies are affected and struggle to quantify their
cyberattack exposure. Therefore, novel approaches must be placed to support the
understanding of the financial loss due to cyberattacks. This article
introduces the Real Cyber Value at Risk (RCVaR), an economical approach for
estimating cybersecurity costs using real-world information from public
cybersecurity reports. RCVaR identifies the most significant cyber risk factors
from various sources and combines their quantitative results to estimate
specific cyberattacks costs for companies. Furthermore, RCVaR extends current
methods to achieve cost and risk estimations based on historical real-world
data instead of only probability-based simulations. The evaluation of the
approach on unseen data shows the accuracy and efficiency of the RCVaR in
predicting and managing cyber risks. Thus, it shows that the RCVaR is a
valuable addition to cybersecurity planning and risk management processes
Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence.
OBJECTIVE
Patients with rotator cuff tears present often with glenohumeral joint instability. Assessing anatomic angles and shoulder kinematics from fluoroscopy requires labelling of specific landmarks in each image. This study aimed to develop an artificial intelligence model for automatic landmark detection from fluoroscopic images for motion tracking of the scapula and humeral head.
MATERIALS AND METHODS
Fluoroscopic images were acquired for both shoulders of 25 participants (N = 12 patients with unilateral rotator cuff tear, 6 men, mean (standard deviation) age: 63.7 ± 9.7 years; 13 asymptomatic subjects, 7 men, 58.2 ± 8.9 years) during a 30° arm abduction and adduction movement in the scapular plane with and without handheld weights of 2 and 4 kg. A 3D full-resolution convolutional neural network (nnU-Net) was trained to automatically locate five landmarks (glenohumeral joint centre, humeral shaft, inferior and superior edges of the glenoid and most lateral point of the acromion) and a calibration sphere.
RESULTS
The nnU-Net was trained with ground-truth data from 6021 fluoroscopic images of 40 shoulders and tested with 1925 fluoroscopic images of 10 shoulders. The automatic landmark detection algorithm achieved an accuracy above inter-rater variability and slightly below intra-rater variability. All landmarks and the calibration sphere were located within 1.5 mm, except the humeral landmark within 9.6 mm, but differences in abduction angles were within 1°.
CONCLUSION
The proposed algorithm detects the desired landmarks on fluoroscopic images with sufficient accuracy and can therefore be applied to automatically assess shoulder motion, scapular rotation or glenohumeral translation in the scapular plane.
CLINICAL RELEVANCE STATEMENT
This nnU-net algorithm facilitates efficient and objective identification and tracking of anatomical landmarks on fluoroscopic images necessary for measuring clinically relevant anatomical configuration (e.g. critical shoulder angle) and enables investigation of dynamic glenohumeral joint stability in pathological shoulders.
KEY POINTS
• Anatomical configuration and glenohumeral joint stability are often a concern after rotator cuff tears. • Artificial intelligence applied to fluoroscopic images helps to identify and track anatomical landmarks during dynamic movements. • The developed automatic landmark detection algorithm optimised the labelling procedures and is suitable for clinical application