Search Results (3)

Artificial intelligence (AI)-based object detection in radiology can assist in clinical diagnosis and treatment planning. This article examines the AI-based object detection models currently used in many imaging modalities, including X-ray Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US). The key models from the convolutional neural network (CNN) as well as the contemporary transformer and hybrid models are analyzed based on their ability to detect pathological features, such as tumors, lesions, and tissue abnormalities. In addition, this review offers a closer look at the strengths and weaknesses of these models in terms of accuracy, robustness, and speed in real clinical settings. The common issues related to these models, including limited data, annotation quality, and interpretability of AI decisions, are discussed in detail. Moreover, the need for strong applicable models across different populations and imaging modalities are addressed. The importance of privacy and ethics in general data use as well as safety and regulations for healthcare data are emphasized. The future potential of these models lies in their accessibility in low resource settings, usability in shared learning spaces while maintaining privacy, and improvement in diagnostic accuracy through multimodal learning. This review also highlights the importance of interdisciplinary collaboration among artificial intelligence researchers, radiologists, and policymakers. Such cooperation is essential to address current challenges and to fully realize the potential of AI-based object detection in radiology. Full article

The national regulatory body in the state regulates the source of ionizing radiation such as diagnostic radiology to minimize exposure to the operators and the patients. The Saudi Food and Drug Authority (KSA-SFDA) cover all medical regulatory aspects of ionizing radiation such as diagnostic X-ray facilities for all practices and intervention requirements. The study presents an assessment and analysis of the level of technical radiation protection requirements and the status of applying the national regulatory standards for different diagnostic facilities in KSA. Based on the online scientific recent database published in the field of radiation protection regulations and dose assessment for diagnostic radiology in KSA, and from the data published by the KSA-SFDA report in 2015. About 109 diagnostic X-ray facilities were selected from 35 governorates distributed in the kingdom. More than 95% of the examined facilities were in good condition concerning the national radiological protection technical regulation. About 11.9% of the facilities had a radiation leakage or cracks in the wall of the X-ray room/entrance door of the room. 16.5% of the facilities did not have a radiation warning sign written in Arabic/English languages. About 21.9% of the operators did not use any personal radiation dosimeter such as TLD or OSL. More than 40.7% of those facilities do not keep a record of the personal dosimeter reading at their facilities. Only 11.1% of the examined facilities do not have any personal protective tools such as a lead apron or thyroid shield. About 38.2% of the examined facilities do not carry out the annual periodic maintenance for the used X-ray machines at those facilities. From the obtained results, it was concluded that the majority of the radiation protection, radiation safety requirements and physical security measures undertaken in these facilities were in good implementation of the national and international technical regulations. The study suggests a need to apply for a quality control test procedures program regularly at those facilities. Full article

The occurrence of barely visible impact damage (BVID) in aircraft composite components and structures being in operation is a serious problem, which threatens structural safety of an aircraft, and should be timely detected and, if necessary, repaired according to the obligatory regulations of currently applied maintenance methodologies. Due to difficulties with a proper detection of such a type of damage even with non-destructive testing (NDT) methods as well as manual evaluation of the testing results, supporting algorithms for post-processing of these results seem to be of a high interest for aircraft maintenance community. In the following study, the authors proposed new approaches for BVID reconstruction based on results of ultrasonic and X-ray computed tomographic testing using authored advanced image processing algorithms. The studies were performed on real composite structures taking into consideration failure mechanisms occurring during impact damaging. The developed algorithms allow extracting relevant diagnostic information both from ultrasonic B-and C-Scans as well as from tomographic 3D arrays used for the validation of ultrasonic reconstructed damage locations, which allows for a significant improvement of the detectability of BVID in tested structures. The developed approach can be especially useful for NDT operators evaluating the results of structural NDT inspections. Full article