This multi-center retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from 6 manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The di-agnostic performance of the device was compared with the ground truth established by the majority agreement of three US board-certified radiologists. Furthermore, the DL-algorithm's time-to-notification was evaluated to demonstrate clinical effectiveness. The study included 1,303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8% – 97.5%] and a specificity of 97.3% [95% CI: 96.2% - 98.1%]. The application classified positive cases by AD types with an accuracy of 99.5% [95% CI: 98.9% – 99.8%] for type A and 97.5 [95% CI: 96.4% – 98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 seconds. This deep learning-based application demonstrated strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents a comprehensive analysis of deep-learning (DL) approach for frequency estimation of single-tones. It is shown that DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques. The study is comprehensive, filling gaps of existing works, where it analyzes error under different signal-to-noise ratios, numbers of nodes, and numbers of input samples; also, under missing SNR information. It is found that DL-based FE is not significantly affected by SNR bias or number of nodes. DL-based approach can work properly using minimal number of input nodes N at which classical methods fail. It is possible for DL to use as little as two layers with two or three nodes each, with complexity of O{N} versus O{Nlog2 (N)} for DFT-based FE, noting that less N is required for DL. Hence, DL can significantly reduce FE complexity, memory, cost, and power consumption, making DL-based FE attractive for resource-limited systems like some IoT sensor applications. Also, reduced complexity opens the door for hardware-efficient implementation using short word-length (SWL) or time-efficient software-defined radio (SDR) communications.