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A proposal for standardized PREMs implementation in hospital context in Portugal
( 2025 ) Passos, Madalena Freitas; Mendes, Inês; Peler, Tayan; Peixoto, Hugo
Patient Reported Experience Measures (PREMs) are essential tools to assess healthcare quality from the patients' perspective, complementing traditional outcome-based evaluations. In Portugal, although healthcare institutions are required to conduct satisfaction surveys, the lack of standardized and interoperable PREMs limits comparability and benchmarking. This paper proposes a framework for the standardized implementation of PREMs in hospitals, supported by interoperability through the HL7 FHIR standard. The solution integrates with hospital information systems to automatically identify clinical encounters, distribute tailored questionnaires, ensure anonymized data collection, and provide real-time analytics through a dashboard with alert mechanisms. The framework was validated in a simulated environment, demonstrating full automation of survey distribution and response analysis, while ensuring patient privacy and usability for healthcare staff. Results indicate that interoperability is a strategic enabler for scalable and sustainable PREMs adoption, fostering continuous quality improvement in healthcare services. Limitations and future work include testing with real hospital systems, expanding to additional clinical contexts, and scaling to national benchmarking.
Comparative study of object detection models for automotive in-vehicle environments
( 2025 ) Ferreira, Diana; Neto, Cristiana; Santos, Ana; Ferreira, Carlos; Fernandes, Duarte; Machado, José Manuel
The rapid progression of autonomous driving and advanced driver-assistance systems has emphasized the need for robust and efficient object detection within vehicle environments. This paper conducts a comparative analysis of state-of-the-art object detection models for in-vehicle environments using public datasets, guided by the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. The study includes several YOLO models (YOLOv5, YOLOv7, YOLOv8, and YOLOvl0), a transformer-based model (RT-DETR), and a CNN-based model incorporating self-attention mechanisms (YOLO-NAS). The evaluation relies on key metrics such as mean Average Precision (mAP) and inference time to determine each model's effectiveness. The experimental results indicate that while our approach to vehicle interior objects detection for forgotten items shows promise, the overall performance fell short of expectations. The YOLOv7 x variant achieved a recall of 0.632 and an mAP50 of 0.351, offering better precision than YOLOv5 in some cases. However, its 10.3-millisecond GPU inference time underscores the challenge of balancing accuracy and computational efficiency. The findings emphasize the impact of dataset quality and class balance in achieving robust and reliable object detection, highlighting the need for tailored datasets to enhance model reliability in real-world automotive applications. This research contributes to the broader vision of green and smart cities by advancing technologies that improve vehicle safety and support sustainable urban mobility systems.
Computer vision approaches for the assessment of corneal ectasia screening
( 2026 ) Passos, Madalena Freitas; PELLER, TAYAN; Mendes, Inês; Sá, Daniel Cunha; Miranda, Vasco; Abreu, Ana; Peixoto, Hugo; Machado, José Manuel; Abelha, António
Corneal ectatic disorders, including keratoconus and its subclinical forms, pose a significant challenge in refractive surgery screening due to their progressive nature and the difficulty of early detection using conventional indices. In recent years, deep learning (DL) approaches have emerged as promising tools for automated corneal image analysis, enabling the extraction of complex spatial patterns directly from imaging data. This study presents a systematic evaluation of multiple deep learning architectures for corneal ectasia screening using Scheimpflug-based corneal maps. A publicly available dataset comprising normal, keratoconus, and suspect keratoconus cases was used to train and evaluate convolutional neural networks and transformer-based models under a unified training and validation protocol. Multiple Classification scenarios were explored, including binary (keratoconus vs. normal) and multiclass (keratoconus vs. normal vs. suspect) settings. The results demonstrate that CNN-based models achieve strong and consistent performance in binary Classification, with accuracy exceeding 91%, supporting their potential role as clinical decision support tools for refractive surgery risk assessment. However, performance declined notably in the multiclass setting, particularly for the suspect category, reflecting both the intrinsic ambiguity of intermediate disease stages and limitations associated with case-level labelling. Vision transformer architectures showed inferior performance across all scenarios, likely due to dataset size constraints and training requirements. Overall, this work highlights the strengths and current limitations of DL-based approaches for corneal ectasia screening and underscores the importance of dataset design and annotation granularity to enable robust clinical translation.