Search Results (59)

Background/Objectives: Pregnancy is associated with profound hormonal, hemodynamic, and metabolic changes that may affect the auditory and vestibular systems. However, evidence regarding pregnancy-related audiological disturbances remains limited due to the low incidence of these conditions and the scarcity of dedicated studies. The study aimed to identify the most commonly reported audiological changes occurring during pregnancy and the postpartum period with an emphasis on sudden sensorineural hearing loss (SSNHL), its risk factors, clinical presentation, and treatment methods. Methods: A literature search was conducted in PubMed, Embase, Web of Science, and Google Scholar from database inception to November 2025 using combinations of the keywords “pregnancy”, “hearing loss”, and “audiological changes”. The review is based on twenty-four original articles that met the preselected criteria. Results: Current evidence does not support pregnancy as an independent risk factor for SSNHL; the prevalence in obstetric patients is lower than in the control groups. Most cases occur during the third trimester or the postpartum period. Pre-pregnancy obesity and elevated blood pressure were identified as potential risk factors. Higher income levels and rural residency are associated with an increased incidence of SSNHL, whereas gestational diabetes, pre-eclampsia, and pregnancy-related weight gain showed no consistent association with SSNHL. Audiometric studies demonstrated mild, predominantly low-frequency, hearing threshold elevations that were generally transient and resolved after delivery. Tinnitus, nausea, aural fullness, dizziness, and vertigo were the most common ear-related complaints. Most of these disorders resolve spontaneously, but some studies suggest steroid treatment should be introduced. Intratympanic corticosteroid therapy was the most frequently investigated treatment for SSNHL and was associated with favorable hearing outcomes in most reported cases. Limited evidence suggests that Dextran 40 may improve hearing outcomes when used as an adjunctive therapy. Conclusions: Potentially transient cochleovestibular impairment during pregnancy and the postpartum period should not be underestimated and requires further evaluation. Pregnancy-related physiological adaptations may contribute to transient auditory and vestibular disturbances, particularly during late pregnancy and the postpartum period. Although current evidence does not indicate an increased risk of SSNHL during pregnancy, prompt recognition and management remain important to prevent long-term hearing impairment. Further high-quality studies are required to clarify the underlying mechanisms and establish evidence-based treatment recommendations for pregnant patients with SSNHL. Full article

Background and Clinical Significance: To report an unusual case of a bilateral low-frequency air–bone gap consistent with an apparent conductive audiometric pattern following spinal anesthesia and discuss a possible underlying mechanism; Case Presentation: A 56-year-old man underwent elective inguinal hernia repair under spinal anesthesia. On the second postoperative day, he developed a severe postural headache followed by bilateral hearing loss. Otoscopic examination was normal. Tuning fork tests and pure-tone audiometry demonstrated a bilateral low-frequency air–bone gap consistent with an apparent conductive audiometric pattern. Laboratory findings were unremarkable. The patient was managed conservatively with bed rest, hydration and systemic corticosteroids, resulting in gradual clinical improvement; Conclusions: Hearing loss after spinal anesthesia is typically sensorineural and attributed to cerebrospinal fluid pressure alterations. This case highlights a rare apparent conductive audiometric pattern in the absence of clinically evident middle-ear pathology. A possible mechanism may involve altered inner-ear pressure dynamics leading to transient mechanical restriction of stapes mobility. Awareness of this atypical presentation may facilitate prompt recognition and appropriate management. Full article

Vertigo, dizziness, and oculomotor disturbances may occur as manifestations of immune-mediated disorders affecting the inner ear, central vestibular pathways, or multisystem autoimmune disease. Although uncommon, these conditions are clinically important because delayed recognition may lead to irreversible hearing loss, vestibular dysfunction, or neurological disability. This review summarizes the clinical presentation, diagnostic approach, and treatment of immune-mediated vestibular and oculomotor disorders. We suggest a practical classification into isolated immune-mediated inner ear disease, systemic autoimmune disorders with audio-vestibular involvement, and autoimmune disorders of the central or peripheral nervous system affecting balance and eye movements. Red flags for such conditions include bilateral or progressive symptoms, fluctuating audio-vestibular deficits, associated neurological signs, and accompanied autoimmune disease. Corticosteroids remain the main first-line treatment in many of these disorders, mainly due to missing data from controlled trials. Steroid-sparing immunosuppressants, biologics, and tumor-directed therapies are effective in many cases; however, because of the missing data, they are only used in selected entities without any other choice. A structured neuro-otological and immunological workup is essential to improve diagnostic accuracy and enable timely therapy. Full article

Electroencephalography (EEG) is a core technique for brain activity monitoring. However, conventional EEG systems suffer from complicated setup and poor portability, which drives the development of ear EEG technology. Ear EEG is divided into in-ear and around-ear types, both with unique application strengths. This review mainly discusses in-ear EEG, as it features a compact structure and fits well with daily wearable use cases. Current research on in-ear EEG is limited to feasibility verification and small-sample experiments. Researchers have not yet combined personalized design with signal processing algorithms systematically, and multi-center clinical trials are still absent. These issues have become the major bottleneck hindering its clinical transformation. This paper reviews the latest advances in ear-EEG systems, focusing on structural innovation and material development to summarize key achievements in hardware design. It also summarizes its typical applications in brain-computer interfaces (BCI), covering steady-state responses, event-related potentials and motor imagery. Meanwhile, it analyzes the application of in-ear EEG in brain state monitoring, including sleep tracking, epilepsy detection, drowsiness evaluation and emotion recognition. Finally, future directions for in-ear EEG are outlined, including personalized design and intelligent signal processing. This review provides a technical framework for beginners and identifies key directions for future research. Full article

Background/Objectives: Anthropometric measurements provide essential normative datasets that form the foundation for clinical practice and forensic identification. The human ear is a highly informative structure due to its complex morphology and individual specificity, making it a valuable tool for biometric systems. This study aimed to estimate biological sex based on auricular morphometric measurements, develop a logistic regression model for this purpose, and validate its performance using ROC analysis. Materials and Methods: This cross-sectional study included 120 adult participants (60 males, 60 females). Standardized digital photographs were analyzed in ImageJ to record 22 linear and 6 angular measurements using established anatomical landmarks. LASSO logistic regression was employed for variable selection and model shrinkage. The final model’s discriminative performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), the Hosmer–Lemeshow test, and the Brier score. Results: A comparative analysis revealed that most linear and angular measurements showed significant sexual dimorphism. Almost all linear dimensions (A1–A22) were significantly larger in males (p < 0.001). Auricular width (A2) and width at the level of the tragus (A3) emerged as the most robust indicators, demonstrating “very large” effect sizes. Conversely, the angle between the preauricular line and the vertical plane (A28) was significantly greater in females, providing a unique inverse relationship for sex estimation. A parsimonious 5-predictor model (incorporating A2, A3, A5, A10, and A28) achieved exceptional discriminative performance with an AUC of 0.980. Conclusions: Auricular morphometry is a highly effective tool for sex estimation. The findings confirm significant sexual dimorphism in the external ear, particularly in linear dimensions. The developed model may serve as a preliminary morphometric reference for future automated biometric recognition studies, although no artificial intelligence-based classification model was developed in the present study. Full article

Background: Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), represents a chronic immune-mediated disorder frequently associated with extraintestinal manifestations. While musculoskeletal, dermatologic, and ocular complications are well recognized, ear, nose, and throat (ENT) involvement remains underrecognized despite its potential morbidity. Objective: To systematically evaluate the spectrum of ENT manifestations in IBD, focusing on clinical presentation, diagnostic approaches, and outcomes. Methods: A systematic literature search was conducted in PubMed and Scopus in accordance with PRISMA 2020 guidelines. Eligible studies included English-language human studies (2015–2026) reporting ENT manifestations in UC or CD. Following screening, 23 studies were included in the qualitative synthesis. Extracted data comprised study design, IBD subtype, patient demographics, ENT manifestations, diagnostic methods, and clinical outcomes. Results: The majority of studies consisted of case reports and small observational series. Sensorineural hearing loss (SNHL) was the most frequently reported manifestation in both adult and pediatric populations, with evidence suggesting immune-mediated mechanisms and variable responsiveness to corticosteroids. Nasal involvement included pyoderma gangrenosum, pyoderma vegetans, and aseptic nasal septal abscess, occasionally resulting in severe structural complications such as saddle-nose deformity. Laryngeal and airway involvement included dysphonia, tracheitis, and rare but potentially life-threatening inflammatory airway disease. Additional findings included associations with chronic rhinosinusitis. Diagnosis relied on audiometry, imaging, endoscopy, and histopathology. Systemic corticosteroids were frequently effective; however, delayed recognition may lead to irreversible sequelae. Conclusions: ENT manifestations in IBD constitute a clinically heterogeneous but important group of extraintestinal complications. Increased awareness of ENT manifestations may support earlier diagnosis and multidisciplinary management of IBD, potentially reducing irreversible complications. Full article

Recent advances in ear biometrics have yielded increasingly accurate detection and recognition methods, driven by the ear’s uniqueness and permanence as a non-invasive biometric modality. Nonetheless, several limitations persist, including computationally demanding models, inconsistent evaluation metrics, and portable systems restricted by manual capture and limited datasets. To address these challenges, we developed a lightweight, video-based ear biometric system implemented on the Raspberry Pi 5. The system integrates You Only Look Once Version 12 (YOLOv12) for ear detection, EfficientNet-4 for feature extraction, and k-Nearest Neighbors (k-NNs) for recognition. Its robust hardware platform combines Raspberry Pi 5 with the Raspberry Pi AI Camera and AI HAT+. To train, fine-tune, and optimize YOLOv12 and EfficientNet-4, we used the Visual Geometry Group (VGG)Face-Ear dataset for training and the Unconstrained Ear Recognition Challenge 2019 dataset for validation, with k-NN employed for classification. The system is evaluated for classification accuracy and system-level performance. 13 participants, comprising 10 enrolled and three unenrolled subjects, participated in testing the system. The enrolled participants registered in the system were correctly identified, whereas unenrolled participants were excluded and rejected. The system achieved 92.31% accuracy, 95.45% precision, 96.97% recall, and an F1-score of 0.95, confirming the feasibility of deploying advanced ear biometric methods on embedded, resource-constrained devices. Full article

The current study proposes an improved DenseNet-based Sequential Multimodal Biometric Authentication System, involving face and ear modality for better human identification. The architecture is composed of three convolutional layers and two dense layers, which are optimized for obtaining the discriminative spatial representations in 200 × 200 pixel facial and ear images. Evaluation is performed based on strict 5-fold subject disjoint cross-validation data to ensure the unbiased assessment. The model proposed attained a steady classification accuracy of 97.1 ± 0.79%, and balanced values for Precision, Recall and F1-score under controlled validation conditions, while the Performance analysis including False Acceptance (FAR), False Rejection (FRR) and Equal Error Rate (EER) showed that the EER found is around 1.05% at the optimum operating value. Comparative experiments between parallel feature concatenation and sequential verification techniques show that the sequential framework yields decreased FAR, when compared to the parallel framework, without having a detrimental effect on overall accuracy, while the Statistical validation by analysis of variance shows that the incremental architectural improvements have a significant impact on performance improvements. Findings of this analysis show a “score distribution” that both “single-trait and traditional multifactor systems” exceed the presentation of a novel method for Nex-G authentication solutions. This study advances biometric security by demonstrating how multimodal fusion may address the increasing global demand for robust and privacy-aware authentication methods, thereby setting a standard for intelligent multimodal recognition systems. Full article

Objective: Sound field (SF) audiometry is widely used to evaluate aided hearing performance. This study compared the test–retest reliability and between-system equivalence of SF audiometry between a newly designed SF system for small audiometric booths and a conventional SF system. Methods: Thirty-nine adults using hearing aids (56 tested ears; 19 females [26 ears] and 20 males [30 ears]) underwent aided warble-tone audiometry (0.25, 0.5, 1, 2, and 4 kHz) and aided speech audiometry using a conventional SF system (two loudspeakers at 45° azimuth and 1 m distance) and a newly designed small-booth SF system (two height-adjustable loudspeakers at 45° azimuth and at 30 cm distance). The same sequence was repeated to assess test–retest performance. The test–retest differences were evaluated using paired t-tests, and the variability was summarized using the coefficient of variation (CV). Equivalence of aided warble-tone thresholds between systems was evaluated using two one-sided tests (TOST) with a ±10 dB margin. Results: Within each system, aided warble-tone thresholds and aided speech reception thresholds (SRTs) did not differ significantly between the test and the retest (all p > 0.05). The warble-tone thresholds were equivalent between systems within the predefined ±10 dB margin by TOST. Aided word recognition scores (WRSs) at 65 dB HL were higher in the newly designed system than in the conventional system (p < 0.0001). The CVs were low in both systems and were slightly lower in the newly designed system. Conclusions: When the listener-to-loudspeaker position is strictly controlled, SF audiometry provides stable test–retest results in hearing-aid users. The newly designed SF system for small audiometric booths produced aided thresholds equivalent to those of the conventional SF system and yielded higher WRSs at 65 dB HL under the tested conditions. Full article

We developed an AI-based system for facial acupoint recognition and healthcare support, integrating MediaPipe facial and hand tracking technologies to address the problems of inaccurate and non-standardized acupoint identification in traditional Chinese medicine (TCM). By leveraging facial landmark detection and fingertip tracking, the system enables accurate localization of facial acupoints to ensure precise stimulation. The system contributes to the standardization of acupoint recognition, intelligent health consultation, and the digital transformation of TCM practices. Further enhancements are necessary by expanding acupoint recognition to other body parts (e.g., ears, hands, feet, and back) and integrating with wearable devices to further promote personalized and precise TCM healthcare. Full article

The integration of wearable inertial measurement units (IMU) in animal welfare Internet of Things (IoT) systems has become crucial for monitoring animal behaviors and enhancing welfare management. However, the vulnerability of IoT devices to network and hardware attacks poses significant risks, potentially compromising data integrity and misleading caregivers, negatively impacting animal welfare. Additionally, current animal monitoring solutions often rely on intrusive tagging methods, such as Radio Frequency Identification (RFID) or ear tagging, which may cause unnecessary stress and discomfort to animals. In this study, we propose a lightweight integrity and provenance-oriented security stack that complements standard transport security, specifically tailored to IMU-based animal motion IoT systems. Our system utilizes a 1D-convolutional neural network (CNN) model, achieving 88% accuracy for precise motion recognition, alongside a lightweight behavioral fingerprinting CNN model attaining 83% accuracy, serving as an auxiliary consistency signal to support collar–animal association and reduce mis-attribution risks. We introduce a dynamically generated pre-shared key (PSK) mechanism based on SHA-256 hashes derived from motion features and timestamps, further securing communication channels via application-layer Hash-based Message Authentication Code (HMAC) combined with Message Queuing Telemetry Transport (MQTT)/Transport Layer Security (TLS) protocols. In our design, MQTT/TLS provides primary device authentication and channel protection, while behavioral fingerprinting and per-window dynamic–HMAC provide auxiliary provenance cues and tamper-evident integrity at the application layer. Experimental validation is conducted primarily via offline, dataset-driven experiments on a public canine IMU dataset; system-level overhead and sensor-to-edge latency are measured on a Raspberry Pi-based testbed by replaying windows through the MQTT/TLS pipeline. Overall, this work integrates motion recognition, behavioral fingerprinting, and dynamic key management into a cohesive, lightweight telemetry integrity/provenance stack and provides a foundation for future extensions to multi-species adaptive scenarios and federated learning applications. Full article

Long COVID-19 syndrome (or so-called post-COVID-19) is indicated by miscellaneous symptoms, usually starting 3 months from the COVID-19 infection and lasting for at least 2 months, which cannot be explained by an alternative diagnosis. There has been more and more reports addressing the audiovestibular dysfunction related to long COVID-19 syndrome. Emerging evidence suggests that the linkage between audiovestibular dysfunction and long COVID-19 syndrome might rely on (a) direct inner ear system damage related to viral invasion and consequent inflammation, (b) micro thromboembolic events, which might result from the COVID-19-induced autoimmune reaction against endothelial cells, and consequent transient-ischemia and hypoxia of the auditory pathways, (c) the disturbed nerve conduction in vestibulocochlear nerves due to viral invasion, and finally (d) altered auditory cortex function, either imbalanced central gain or neurotransmitter disturbance. However, most of the aforementioned mechanism remained hypothetic and still needed further studies to approve or refute. This systematic review synthesizes current evidence on the characteristics, pathophysiology, diagnostic approaches, and management of audiovestibular dysfunction related to long COVID-19 syndrome. Literature searches across PubMed, Embase, ClinicalKey, Web of Science, and ScienceDirect (up to 15 December 2025) were conducted in accordance with PRISMA guidelines. Through this systematic review, we provided a schematic diagram of the physiopathology of long COVID-19 syndrome-related audiovestibular dysfunction. Further, we summarized the currently available diagnostic tools to explore the audiovestibular function in such patients. The currently available treatment, either pharmacotherapy or nonpharmacotherapy, mainly tackles idiopathic audiovestibular dysfunction but not specifically long COVID-19 syndrome-related audiovestibular dysfunction. Timely recognition and intervention may prevent progression to permanent hearing loss or vestibular disability, improving quality of life. Trial registration: PROSPERO CRD420251265741. Full article

Car accidents remain a leading cause of death worldwide, with drowsiness and distraction accounting for roughly 25% of fatal crashes in Ecuador. This study presents a real-time driver monitoring system that uses computer vision and deep learning to detect fatigue, distraction, and emotions from facial expressions. It combines a MobileNetV2-based CNN trained on RAF-DB for emotion recognition and MediaPipe’s 468 facial landmarks to compute the EAR (Eye Aspect Ratio), the MAR (Mouth Aspect Ratio), the gaze, and the head pose. Tests with 27 participants in both real and simulated driving environments showed strong results. There was a 100% accuracy in detecting distraction, 85.19% for yawning, and 88.89% for eye closure. The system also effectively recognized happiness (100%) and anger/disgust (96.3%). However, it struggled with sadness and failed to detect fear, likely due to the subtlety of real-world expressions and limitations in the training dataset. Despite these challenges, the results highlight the importance of integrating emotional awareness into driver monitoring systems, which helps reduce false alarms and improve response accuracy. This work supports the development of lightweight, non-invasive technologies that enhance driving safety through intelligent behavior analysis. Full article

Mature soybean pods exhibit high homogeneity in color and texture relative to straw and dead leaves, and instances are often densely occluded, posing significant challenges for accurate field segmentation. To address these challenges, this paper constructs a high-quality field-based mature soybean dataset and proposes an adaptive Transformer-based network, PodFormer, to improve segmentation performance under homogeneous backgrounds, dense distributions, and severe occlusions. PodFormer integrates three core innovations: (1) the Adaptive Wavelet Detail Enhancement (AWDE) module, which strengthens high-frequency boundary cues to alleviate weak-boundary ambiguities; (2) the Density-Guided Query Initialization (DGQI) module, which injects scale and density priors to enhance instance detection in both sparse and densely clustered regions; and (3) the Mask Feedback Gated Refinement (MFGR) layer, which leverages mask confidence to adaptively refine query updates, enabling more accurate separation of adhered or occluded instances. Experimental results show that PodFormer achieves relative improvements of 6.7% and 5.4% in mAP50 and mAP50-95, substantially outperforming state-of-the-art methods. It further demonstrates strong generalization capabilities on real-world field datasets and cross-domain wheat-ear datasets, thereby providing a reliable perception foundation for structural trait recognition in intelligent soybean harvesting systems. Full article

Real-time detection of fresh corn ear height can provide a basis for dynamic adjustment of harvester header parameters, reducing mechanical damage and improving harvest quality. This study proposes a corn ear height detection model (CEHD). A YOLO-HAMDF network is developed for ear recognition, in which the core modules—TBDA, GLSA, and AQE—respectively suppress background interference, enhance contextual perception, and optimize bounding-box scoring. Depth information is incorporated to filter non-target regions and improve system robustness. In addition, a DI-DeepSORT module is designed for ear tracking, where DBC-Net and IDA-Kalman, respectively, enhance the discriminability of ReID features and enable independent-dimension adaptive noise modeling with smoothed positional updates. Experimental results demonstrate that the proposed CEHD model achieves a mean absolute error (MAE) of only 3.21 ± 0.05 cm under field conditions, indicating strong stability and practical applicability. In summary, this study presents a stable and reliable corn ear height detection system, achieves real-time monitoring of ear height, and provides data support for the dynamic adjustment of header parameters in fresh corn harvesters. Full article