Periodontal diseases, ranging from gingivitis to periodontitis, are prevalent oral diseases affecting over 50% of the global population. These diseases arise from infections and inflammation of the gums and supporting bones, significantly impacting oral health. The established link between periodontal diseases and systemic diseases, such as cardiovascular diseases, underscores their importance as a public health concern. Consequently, the early detection and prevention of periodontal diseases have become critical objectives in healthcare, particularly through the integration of advanced artificial intelligence (AI) technologies. This paper aims to bridge the gap between clinical practices and cutting-edge technologies by providing a comprehensive review of current research. We examine the identification of causative factors, disease progression, and the role of AI in enhancing early detection and treatment. Our goal is to underscore the importance of early intervention in improving patient outcomes and to stimulate further interest among researchers, bioengineers, and AI specialists in the ongoing exploration of AI applications in periodontal disease diagnosis.

Polycyclic aromatic hydrocarbons (PAHs) are increasingly recognized as a major contributor to the occupational cancer risk among firefighters. In response, the National Fire Protection Association (NFPA) and other regulatory bodies have recommended rigorous decontamination protocols to minimize PAH exposure. Despite these efforts, a critical gap persists: the absence of real-time, field-deployable devices capable of detecting these invisible and toxic compounds during firefighting operations or within fire stations. Additionally, the lack of effective and optimized methods for the removal of these hazardous substances from the immediate environments of firefighters continues to pose a serious occupational health challenge. Although numerous studies have investigated PAH detection in environmental contexts, current technologies are still largely confined to laboratory settings and are unsuitable for field use. This review critically examines recent advances in PAH decontamination strategies for firefighting and explores alternative sensing solutions. We evaluate both conventional analytical methods, such as gas chromatography, high-performance liquid chromatography, and mass spectrometry, and emerging portable PAH detection technologies. By highlighting the limitations of existing systems and presenting novel sensing approaches, this paper aims to catalyze innovation in sensor development. Our ultimate goal is to inspire the creation of robust, field-deployable tools that enhance decontamination practices and significantly improve the health and safety of firefighters by reducing their long-term risks of cancer.

Oscillometric blood pressure (BP) measurement devices are widely utilized as the primary automated BP measurement tools in non-specialist environments. However, their accuracy and reliability vary under different settings and for different age groups and health conditions. An essential constraint of current oscillometric BP measurement devices is their analysis algorithms' incapacity to capture the BP information encoded in the pattern of recorded oscillometric pulses to its fullest extent. In this paper, we propose a new 2D oscillometric data representation that enables a full characterization of arterial system and empowers the application of deep learning to extract the most informative features correlated with BP. A hybrid convolutional-recurrent neural network was developed to capture the oscillometric pulses morphological information as well as their temporal evolution over the cuff deflation period from the 2D structure, and estimate BP. The performance of the proposed method was verified on three oscillometric databases collected from the wrist and upper arms of 245 individuals. It was found that it achieves a mean error and a standard deviation of error of as low as 0.08 mmHg and 2.4 mmHg in the estimation of systolic BP, and 0.04 mmHg and 2.2 mmHg in the estimation of diastolic BP, respectively. Our proposed method outperformed the state-of-the-art techniques and satisfied the current international standards for BP monitors by a wide margin. The proposed method shows promise toward robust and objective BP estimation in a variety of patients and monitoring situations.

Idiopathic ventricular arrhythmias (IVAs) are extra abnormal heartbeats disturbing the regular heart rhythm that can become fatal if left untreated. Cardiac catheter ablation is the standard approach to treating IVAs; however, a crucial prerequisite for the ablation is the localization of the IVA origin. The current IVA localization techniques are invasive, rely on expert interpretation, or are inaccurate. This study aims to develop a deep learning algorithm that can automatically identify the origin of IVAs from ECG signals without the need for expert manual analysis. Our developed deep learning algorithm comprises a spatial feature extraction to extract the most informative features from multichannel ECG data, temporal modeling to capture the evolving pattern of the ECG time series and an attention mechanism to weigh the most important temporal features and improve the model’s interpretability. The algorithm was validated on a 12-lead ECG dataset collected from 334 patients (230 females, age 46±13) who experienced IVAs and successfully underwent a catheter ablation procedure that determined the IVAs’ exact origins. It achieved an area under the curve (AUC) of 93%, an accuracy of 94%, a sensitivity of 97%, a precision of 95%, and an F1 score of 96% in locating the origin of IVAs and outperformed existing automatic and semi-automatic algorithms. Our proposed method shows promise toward automatic and noninvasive evaluation of IVA patients before cardiac catheter ablation.

Precise 3D segmentation of cerebral vasculature from T1-weighted contrast-enhanced (T1CE) MRI is crucial for safe neurosurgical planning. Manual delineation is time-consuming and prone to inter-observer variability, while current automated methods often trade accuracy for computational cost, limiting clinical use. We present NeuroVascU-Net, the first deep learning architecture specifically designed to segment cerebrovascular structures directly from clinically standard T1CE MRI in neuro-oncology patients, addressing a gap in prior work dominated by TOF-MRA-based approaches. NeuroVascU-Net builds on a dilated U-Net and integrates two specialized modules: a Multi-Scale Contextual Feature Fusion ($MSC^2F$) module at the bottleneck and a Cross-Domain Adaptive Feature Fusion ($CDA^2F$) module at deeper hierarchical layers. $MSC^2F$ captures both local and global information via multi-scale dilated convolutions, while $CDA^2F$ dynamically integrates domain-specific features, enhancing representation while keeping computation low. The model was trained and validated on a curated dataset of T1CE scans from 137 brain tumor biopsy patients, annotated by a board-certified functional neurosurgeon. NeuroVascU-Net achieved a Dice score of 0.8609 and precision of 0.8841, accurately segmenting both major and fine vascular structures. Notably, it requires only 12.4M parameters, significantly fewer than transformer-based models such as Swin U-NetR. This balance of accuracy and efficiency positions NeuroVascU-Net as a practical solution for computer-assisted neurosurgical planning.

Recent research has identified salivary oral polymorphonuclear neutrophils (oPMNs) as reliable cellular biomarkers for monitoring the progression of periodontal disease (PD). While conventional diagnostic tools such as periodontal probing and radiographic imaging are effective at detecting advanced disease stages, they lack the sensitivity required for early diagnosis. oPMNs, derived from circulating white blood cells (WBCs), transmigrate through the oral epithelium and appear in saliva with diverse morphological characteristics. Although deep learning-based WBC quantification has been extensively explored using peripheral blood datasets, the unique morphology of oPMNs necessitates the development of new image datasets and retraining of models tailored to their detection. To address these challenges, we introduce DePerio-an AI-powered diagnostic pipeline that integrates a novel oPMN isolation protocol with deep neural network (DNN) architectures. We evaluate the performance of multiple DNN models to achieve accurate detection and quantification of oPMNs. Validated against standard quantification methods for oPMNs and oral inflammatory load (OIL), DePerio achieved a detection error rate of less than 5%. In a clinical study involving 111 human saliva samples, spanning individuals from healthy to severe periodontitis cases, and successfully classified five distinct OIL levels. This robust and low-complexity platform provides a scalable and practical solution for early PD detection and longitudinal monitoring in clinical dental practice.