Despite widespread discussion of digital transformation, many organizations struggle to assess their digital capability and define improvement priorities. We developed a transparent self-assessment tool, implemented as a Streamlit web application, based on 25 items grouped into five dimensions. Dimension averages are combined into a weighted overall score (1-5 scale) using explicit and visible scoring rules. The tool was evaluated through a single organizational case study (XL Labs) and a pilot expert review (N = 3), providing preliminary, non-generalizable evidence. In the case study, the organization achieved a score of 3.41 / 5.00, corresponding to the Intermediate maturity category, which remained stable under one-at-a-time sensitivity analysis (±0. 20 per dimension).

BACKGROUND AND OBJECTIVE Peripheral artery disease (PAD) is a progressive vascular condition affecting >237 million individuals worldwide. Accurate diagnosis and patient-specific treatment planning are critical but are often hindered by limited access to advanced imaging tools and real-time analytical support. This study presents DECODE, an open-source, cloud-based platform that integrates artificial intelligence, interactive 3D visualization, and computational modeling to improve the noninvasive management of PAD. METHODS The DECODE platform was designed as a modular backend (Django) and frontend (React) architecture that combines deep learning-based segmentation, real-time volume rendering, and finite element simulations. Peripheral artery and intima-media thickness segmentation were implemented via convolutional neural networks, including extended U-Net and nnU-Net architectures. Centreline extraction algorithms provide quantitative vascular geometry analysis. Balloon angioplasty simulations were conducted via nonlinear finite element models calibrated with experimental data. Usability was evaluated via the System Usability Scale (SUS), and user acceptance was assessed via the Technology Acceptance Model (TAM). RESULTS Peripheral artery segmentation achieved an average Dice coefficient of 0.91 and a 95th percentile Hausdorff distance 1.0 mm across 22 computed tomography dataset. Intima-media segmentation evaluated on 300 intravascular optical coherence tomography images demonstrated Dice scores 0.992 for the lumen boundaries and 0.980 for the intima boundaries, with corresponding Hausdorff distances of 0.056 mm and 0.101 mm, respectively. Finite element simulations successfully reproduced the mechanical interactions between balloon and artery models in both idealized and subject-specific geometries, identifying pressure and stress distributions relevant to treatment outcomes. The platform received an average SUS score 87.5, indicating excellent usability, and an overall TAM score 4.21 out of 5, reflecting high user acceptance. CONCLUSIONS DECODE provides an automated, cloud-integrated solution for PAD diagnosis and intervention planning, combining deep learning, computational modeling, and high-fidelity visualization. The platform enables precise vascular analysis, real-time procedural simulation, and interactive clinical decision support. By streamlining image processing, enhancing segmentation accuracy, and enabling in-silico trials, DECODE offers a scalable infrastructure for personalized vascular care and sets a new benchmark in digital health technologies for PAD.

BackgroundDialysis is a very complex treatment that is received by around 3 million people annually. Around 10% of the death cases in the presence of the dialysis machine were due to the technical errors of dialysis devices. One of the ways to maintain dialysis devices is by using machine learning and predictive maintenance in order to reduce the risk of patient's death, costs of repairs and provide a higher quality treatment.ObjectivePrediction of dialysis machine performance status and errors using regression models.MethodThe methodology includes seven steps: data collection, processing, model selection, training, evaluation, fine-tuning, and prediction. After preprocessing 1034 measurements, twelve machine learning models were trained to predict dialysis machine performance, and temperature and conductivity error values.ResultsEach model was trained 100 times on different splits of the dataset (80% training, 10% testing, 10% evaluation). Logistic regression achieved the highest accuracy in predicting dialysis machine performance. For temperature predictions, Lasso regression had the lowest MSE on training data (0.0058), while Linear regression showed the highest R² (0.59). For conductivity predictions, Lasso regression provided the lowest MSE (0.134), with Decision tree achieving the highest R² (0.2036). SVM attained the lowest MSE on testing dataset, with 0.0055 for temperature and 0.1369 for conductivity.ConclusionThe results of this study demonstrate that clinical engineering (CE) and health technology management (HTM) departments in healthcare institutions can benefit from proposed automated systems for advanced management of dialysis machines.

Background Poorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. As infant incubators are used as a form of fundamental healthcare support for the most sensitive population, prematurely born infants, special care mus be taken to ensure their proper functioning. This is done through a standardized process of post-market surveillance. Objective To address the issue of faulty infant incubators being undetected and used between yearly post-market surveillance, an automated system based on machine learning was developed for prediction of infant incubator performance status. Methods In total, 1997 samples were collected during the inspection process of infant incubator inspections performed by an ISO 17020 accredited laboratory at various healthcare institutions in Bosnia and Herzegovina. Various machine learning algorithms were considered, including Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) for the development of the automated system. Results The aforementioned algorithms were selected because of their ability to handle large datasets and their potential for achieving high prediction accuracy. The 0.93 AUC of Naïve Bayes indicates that it is overall stronger in predictive capabilities than decision tree and random forest which displayed superior accuracy in comparison to Naïve Bayes. Conclusion The results of this study demonstrate that machine learning algorithms can be effectively used to predict infant incubator performance status on the basis of measurements taken during post-market surveillance. Adoption of these automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of infant incubators that are already being used in healthcare institutions.

Background After 25 years of implementing the Medical Devices Directive (MDD), in 2017, the new Medical Devices Regulation (MDR) came into force, establishing stricter requirements for post-market surveillance of the safety and performance of medical devices (MD). For electrocardiogram (ECG) devices, which are crucial for monitoring cardiac activities, these requirements are essential to ensure the reliability and accuracy of diagnosing cardiac conditions and timely treatment. Objective This study aims to enhance post-market surveillance of ECG devices by leveraging Machine Learning (ML) algorithms to predict the operational status of these devices. Specifically, the research focuses on classifying the success or failure of ECG device operations based on performance and safety parameters. The ultimate goal is to improve the management strategies of ECG devices in healthcare institutions, ensuring optimal functionality and increasing the reliability of diagnostic procedures. Method During the inspection process of ECG devices conducted by an accredited laboratory in accordance with ISO 17020 standard in numerous healthcare institutions in Bosnia and Herzegovina, a total of 5577 samples were collected. Various machine learning algorithms, including Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Gaussian Naive Bayes (NB), and Support Vector Machine (SVM), were employed for result comparison and selection of the most accurate algorithm. Results All algorithms demonstrated good performance, but the Random Forest (RF) algorithm stood out, achieving 100% accuracy in predicting the success/unsuccess status of the device. While the results of this research are specific to the collected data from EKG devices, the developed algorithms can be applied to other similar datasets, offering opportunities for broader use in the medical environment. Conclusion Implementing machine learning algorithms for automated systems in healthcare institutions can significantly enhance the quality of patient diagnosis and treatment. Additionally, these systems can optimize costs associated with managing medical devices. Improved post-market surveillance using ML can address challenges related to ensuring device reliability and safety.

Background Atherosclerosis of the coronary arteries is a chronic, progressive condition characterized by the buildup of plaque within the arterial walls. Coronary artery disease (CAD), more specifically coronary atherosclerosis (CATS), is one of the leading causes of death worldwide. Computational modeling frameworks have been used for simulation of atherosclerotic plaque progression and with the advancement of agent-based modeling (ABM) the simulation results became more accurate. However, there is a need for optimization of resources for predictive modeling, hence surrogate models are being built to substitute lengthy computational models without compromising the results. Objective This study explores the development of a surrogate model for atherosclerotic plaque progression using ABM simulation data. Method The dataset used for this study contains samples from latin-hypercube sampling based generated simulation parameters used in conjunction with 15 patient-specific geometries and corresponding plaque progression data. The developed surrogate model is based on deep learning using artificial neural networks (ANN). Results The surrogate model achieved an accuracy of 95.4% in benchmarking with the ABM model it was built upon which indicates the robustness of the framework. Conclusion Adoption of surrogate models with high accuracy in practice opens an avenue for utilization of high-fidelity decision support systems for predicting atherosclerotic plaque progression in real-time.