Foreign Body Ingestion: A Common Presentation Among Pediatric Age Group in the City of AlAhsa Eastern Province, Saudi Arabia.

Background Foreign body ingestion is a prevalent issue among children and presents considerable morbidity and mortality rates. Due to children's increased accessibility to electronic toys and equipment, foreign body ingestion has become a common reason for presenting to pediatric emergency departments worldwide. In this context, this research aims to determine the prevalence of foreign body ingestion among children in AlAhsa, Saudi Arabia. Methodology This observational retrospective descriptive study was conducted at Maternity and Children Hospital, AlAhsa, Saudi Arabia, from 2017 to 2021. The study included children (less than 14 years old) who presented to the emergency department with a history of foreign body ingestion. The biographical data, clinical presentation, type of foreign body, and X-ray findings were documented. Results A total of 91 cases of foreign body ingestion or aspiration in children under 14 years of age were included. Approximately half of the patients were under the age of three, and 62.2% of them were male, while 37.8% were female. The clinical presentation revealed that only 24% were symptomatic. Coins were the most commonly ingested foreign bodies (28.9%), followed by metallic objects (20%), and batteries were the least frequently ingested foreign bodies, recorded in eight cases. Conclusion Early detection and treatment of foreign body ingestion is crucial to prevent consequences. In this study, the most frequent foreign bodies detected were coins among children up to three years old. Raising parents' awareness about the prevention of foreign body ingestion is an important step toward reducing its incidence.

Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks.

Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization metrics to evaluate our method. First, the original RGB-colored images are utilized for the training phase, and then compression methods such as channel reduction and sparsity are applied. Our results show an accuracy increase of 6% from RGB on 32-bit (baseline) to the optimized representation of sparsity on RGB with a lower bit-width, i.e., <8:8>. For energy estimation on the used CNN model, we found that the energy used in RGB color mode with 32-bit is considerably higher than the other lower bit-width and compressed color modes. Moreover, we show that lower bit-width implementations yield higher resource utilization and a lower memory bottleneck ratio. This work is suitable for inference on energy-limited devices, which are increasingly being used in the Internet of Things (IoT) systems that facilitate healthcare systems.

Maize Tassel Detection From UAV Imagery Using Deep Learning.

The timing of flowering plays a critical role in determining the productivity of agricultural crops. If the crops flower too early, the crop would mature before the end of the growing season, losing the opportunity to capture and use large amounts of light energy. If the crops flower too late, the crop may be killed by the change of seasons before it is ready to harvest. Maize flowering is one of the most important periods where even small amounts of stress can significantly alter yield. In this work, we developed and compared two methods for automatic tassel detection based on the imagery collected from an unmanned aerial vehicle, using deep learning models. The first approach was a customized framework for tassel detection based on convolutional neural network (TD-CNN). The other method was a state-of-the-art object detection technique of the faster region-based CNN (Faster R-CNN), serving as baseline detection accuracy. The evaluation criteria for tassel detection were customized to correctly reflect the needs of tassel detection in an agricultural setting. Although detecting thin tassels in the aerial imagery is challenging, our results showed promising accuracy: the TD-CNN had an F1 score of 95.9% and the Faster R-CNN had 97.9% F1 score. More CNN-based model structures can be investigated in the future for improved accuracy, speed, and generalizability on aerial-based tassel detection.