Postpartum placental assessment is essential for clarifying adverse pregnancy outcomes and informing clinical decisions; however, anatomopathological examination is generally reserved for selected cases due to structural and operational constraints. This study presents a deep learning-based pipeline for segmenting placental structures in real images taken in the delivery room, aiming to support clinical triage. A proprietary dataset was built using a standardized photographic protocol and annotated by pathologists across nine morphological classes. Five architectures were evaluated: U-Net with ResNet34, ResNet50, EfficientNet-B0, and EfficientNet-B7 backbones, in addition to YOLOv11 for instance segmentation. ResNet34 achieved the best overall performance (Dice: 81.2%, IoU: 70.6%, Accuracy: 85.3%), while YOLOv11 reached a AP50 of 73.2% in detecting key anatomical components. Despite the limitation imposed by the small dataset—which may affect the models’ generalization capability—the results demonstrate the feasibility of using AI for photographic placental triage, with potential to assist clinical decisions and optimize resource use in obstetric settings.

Planning local trajectories for nonholonomic vehicles in real-time that can converge, be feasible, and be smooth simultaneously is a key challenge in autonomous ground vehicle navigation. Many techniques have attempted to overcome this challenge by partially solving a subset of these goals. We propose an approach based on first allowing multiple local path planning algorithms to run in parallel and then choosing the right one based on selection criteria. This allows the use of different specialized types of local planning solutions to account for the execution time, convergence, and path smoothness. Simulations show that the parallel ensemble of local planners performs better than each of its composing local path planners running independently in terms of those goals. Our solution allows the vehicle’s behavior to adapt to different scenarios by automatic local planner selection, which maintains low system complexity and increases adaptability. Moreover, we present an efficient algorithm for local goal approximation of global goals that integrates sub-planning tasks as a single long-reach planning response, enabling the Ensemble to navigate unknown unstructured scenarios.

This systematic literature review investigates the state of the art in the application of artificial intelligence (AI), particularly convolutional neural networks (CNNs), in the diagnosis of pneumoconioses and asbestos-related diseases (ARDs). A total of 30 articles published between 2020 and 2025 were analyzed, selected from major scientific databases (IEEE Xplore, ScienceDirect, Springer Link, ACM Digital Library, Nature, Wiley Online Library). The analysis addressed the models used types of radiological images (chest X-rays and computed tomography), performance metrics, and limitations. A significant advancement was observed in the use of CNNs and 3D architectures, with an emphasis on automated screening and the interpretability of clinical patterns.