To evaluate the repeatability of a novel automated technique called Smart ERA (Smart Endometrial Receptivity Analysis) for the automated segmentation and volume calculation of the endometrium in patients with normal uteri,, and to compare the agreement of endometrial volume measurements between Smart ERA, the semi-automated Virtual Organ Computer-aided Analysis (VOCAL) technique and manual segmentation. This retrospective study evaluated endometrial volume measurement in infertile patients who underwent frozen-thawed embryo transfer (FET). Transvaginal three-dimensional ultrasound scans were performed using a Resona R9 ultrasound machine. Data was collected from patients between 2021 and 2022. Patients with normal uteri and optimal ultrasound images were included. Endometrial volumes were measured using Smart ERA, VOCAL at 15° rotation, and manual segmentation. Intra-observer repeatability and agreement between techniques were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. A total of 407 female patients were evaluated (mean age 33.2 ± 4.7 years). The repeatability of Smart ERA showed an ICC of 0.983 (95% CI 0.984-0.991). The agreement between Smart ERA and the manual method, Smart ERA and VOCAL, and VOCAL and the manual method, as assessed by ICC, were 0.986 (95% CI 0.977-0.990), 0.943 (95% CI 0.934-0.963), and 0.951 (95% CI 0.918-0.969), respectively. The Smart ERA technique required approximately 3 s for endometrial volume calculation, while VOCAL took around 5 min and the manual segmentation method took approximately 50 min. The Smart-ERA software, which employs a novel three-dimensional segmentation algorithm, demonstrated excellent intra-observer repeatability and high agreement with both VOCAL and manual segmentation for endometrial volume measurement in women with normal uteri. However, these findings should be interpreted with caution, as the algorithm\'s performance may not be generalizable to populations with different uterine characteristic. Additionally, Smart ERA required significantly less time compared to VOCAL and manual segmentation.

BACKGROUND: The escalating global prevalence of obesity has necessitated the exploration of novel diagnostic approaches. Recent scientific inquiries have indicated potential alterations in voice characteristics associated with obesity, suggesting the feasibility of using voice as a noninvasive biomarker for obesity detection.
OBJECTIVE: This study aims to use deep neural networks to predict obesity status through the analysis of short audio recordings, investigating the relationship between vocal characteristics and obesity.
METHODS: A pilot study was conducted with 696 participants, using self-reported BMI to classify individuals into obesity and nonobesity groups. Audio recordings of participants reading a short script were transformed into spectrograms and analyzed using an adapted YOLOv8 model (Ultralytics). The model performance was evaluated using accuracy, recall, precision, and F1-scores.
RESULTS: The adapted YOLOv8 model demonstrated a global accuracy of 0.70 and a macro F1-score of 0.65. It was more effective in identifying nonobesity (F1-score of 0.77) than obesity (F1-score of 0.53). This moderate level of accuracy highlights the potential and challenges in using vocal biomarkers for obesity detection.
CONCLUSIONS: While the study shows promise in the field of voice-based medical diagnostics for obesity, it faces limitations such as reliance on self-reported BMI data and a small, homogenous sample size. These factors, coupled with variability in recording quality, necessitate further research with more robust methodologies and diverse samples to enhance the validity of this novel approach. The findings lay a foundational step for future investigations in using voice as a noninvasive biomarker for obesity detection.

BACKGROUND: The digital era has witnessed an escalating dependence on digital platforms for news and information, coupled with the advent of \"deepfake\" technology. Deepfakes, leveraging deep learning models on extensive data sets of voice recordings and images, pose substantial threats to media authenticity, potentially leading to unethical misuse such as impersonation and the dissemination of false information.
OBJECTIVE: To counteract this challenge, this study aims to introduce the concept of innate biological processes to discern between authentic human voices and cloned voices. We propose that the presence or absence of certain perceptual features, such as pauses in speech, can effectively distinguish between cloned and authentic audio.
METHODS: A total of 49 adult participants representing diverse ethnic backgrounds and accents were recruited. Each participant contributed voice samples for the training of up to 3 distinct voice cloning text-to-speech models and 3 control paragraphs. Subsequently, the cloning models generated synthetic versions of the control paragraphs, resulting in a data set consisting of up to 9 cloned audio samples and 3 control samples per participant. We analyzed the speech pauses caused by biological actions such as respiration, swallowing, and cognitive processes. Five audio features corresponding to speech pause profiles were calculated. Differences between authentic and cloned audio for these features were assessed, and 5 classical machine learning algorithms were implemented using these features to create a prediction model. The generalization capability of the optimal model was evaluated through testing on unseen data, incorporating a model-naive generator, a model-naive paragraph, and model-naive participants.
RESULTS: Cloned audio exhibited significantly increased time between pauses (P<.001), decreased variation in speech segment length (P=.003), increased overall proportion of time speaking (P=.04), and decreased rates of micro- and macropauses in speech (both P=.01). Five machine learning models were implemented using these features, with the AdaBoost model demonstrating the highest performance, achieving a 5-fold cross-validation balanced accuracy of 0.81 (SD 0.05). Other models included support vector machine (balanced accuracy 0.79, SD 0.03), random forest (balanced accuracy 0.78, SD 0.04), logistic regression, and decision tree (balanced accuracies 0.76, SD 0.10 and 0.72, SD 0.06). When evaluating the optimal AdaBoost model, it achieved an overall test accuracy of 0.79 when predicting unseen data.
CONCLUSIONS: The incorporation of perceptual, biological features into machine learning models demonstrates promising results in distinguishing between authentic human voices and cloned audio.

Negative schizotypal traits potentially can be digitally phenotyped using objective vocal analysis. Prior attempts have shown mixed success in this regard, potentially because acoustic analysis has relied on small, constrained feature sets. We employed machine learning to (a) optimize and cross-validate predictive models of self-reported negative schizotypy using a large acoustic feature set, (b) evaluate model performance as a function of sex and speaking task, (c) understand potential mechanisms underlying negative schizotypal traits by evaluating the key acoustic features within these models, and (d) examine model performance in its convergence with clinical symptoms and cognitive functioning. Accuracy was good (> 80%) and was improved by considering speaking task and sex. However, the features identified as most predictive of negative schizotypal traits were generally not considered critical to their conceptual definitions. Implications for validating and implementing digital phenotyping to understand and quantify negative schizotypy are discussed.

暂无摘要。

The objective of this manuscript was to review the indications, efficacy, and safety of a 585 nm pulsed dye laser (PDL) in non-malignant laryngeal lesions. Following the PRISMA statement recommendations, three independent authors searched for articles published in PubMed/MEDLINE, the Cochrane Library, Google Scholar, Scielo, and Web of Science. A bias analysis was performed following NICE guidance tools. From the 506 identified publications, 19 observational studies met the inclusion criteria. The PDL improves vocal quality objectively and subjectively in vascular lesions (p < 0.005) and improves vocal quality in patients with dysplasia/leukoplasia without changing the natural history of the disease compared to other treatments. Reinke\'s edema and granulomas require an average of 1.5 PDL sessions for resolution. Treatment of recurrent respiratory papillomatosis requires multiple sessions, with complete remission achieved in 50-70% of patients. Regardless of the lesion, the tolerance of the procedure under local anesthesia is exceptional (84-97%), and the results in terms of regression and vocal quality are promising. The complication rate is minimal, and the procedure does not interfere with other treatment alternatives. There is no consensus on laser settings. The lack of consistent use in evaluating vocal outcomes, whether objective or subjective, prevents the comparability between studies. The 585 nm pulsed dye laser appears to be an effective and safe therapeutic option in patients with non-malignant laryngeal pathology. Future controlled studies are needed to compare the 585 nm pulsed dye laser with other lasers or cold instrument procedures.

Vocal biomarker-based machine learning approaches have shown promising results in the detection of various health conditions, including respiratory diseases, such as asthma.
This study aimed to determine whether a respiratory-responsive vocal biomarker (RRVB) model platform initially trained on an asthma and healthy volunteer (HV) data set can differentiate patients with active COVID-19 infection from asymptomatic HVs by assessing its sensitivity, specificity, and odds ratio (OR).
A logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a data set of approximately 1700 patients with a confirmed asthma diagnosis and a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease, interstitial lung disease, and cough. In this study, 497 participants (female: n=268, 53.9%; <65 years old: n=467, 94%; Marathi speakers: n=253, 50.9%; English speakers: n=223, 44.9%; Spanish speakers: n=25, 5%) were enrolled across 4 clinical sites in the United States and India and provided voice samples and symptom reports on their personal smartphones. The participants included patients who are symptomatic COVID-19 positive and negative as well as asymptomatic HVs. The RRVB model performance was assessed by comparing it with the clinical diagnosis of COVID-19 confirmed by reverse transcriptase-polymerase chain reaction.
The ability of the RRVB model to differentiate patients with respiratory conditions from healthy controls was previously demonstrated on validation data in asthma, chronic obstructive pulmonary disease, interstitial lung disease, and cough, with ORs of 4.3, 9.1, 3.1, and 3.9, respectively. The same RRVB model in this study in COVID-19 performed with a sensitivity of 73.2%, specificity of 62.9%, and OR of 4.64 (P<.001). Patients who experienced respiratory symptoms were detected more frequently than those who did not experience respiratory symptoms and completely asymptomatic patients (sensitivity: 78.4% vs 67.4% vs 68%, respectively).
The RRVB model has shown good generalizability across respiratory conditions, geographies, and languages. Results using data set of patients with COVID-19 demonstrate its meaningful potential to serve as a prescreening tool for identifying individuals at risk for COVID-19 infection in combination with temperature and symptom reports. Although not a COVID-19 test, these results suggest that the RRVB model can encourage targeted testing. Moreover, the generalizability of this model for detecting respiratory symptoms across different linguistic and geographic contexts suggests a potential path for the development and validation of voice-based tools for broader disease surveillance and monitoring applications in the future.

Nasotracheal intubation is commonly performed under general anesthesia in oral and maxillofacial surgery. For the convenience of surgery, nasal Ring-Adair-Elwyn (RAE) tubes are mainly used. Because the nasal RAE tubes were bent in an \"L\" shape, the insertion depth was limited. Particularly, it is necessary to accurately determine the appropriate depth of the RAE tubes in children. Several types of nasal RAE tubes are used in the medical market, which vary in material and length. We performed endotracheal intubation using a nasal RAE tube for double-jaw surgery, but air leakage persisted even when the air pressure in the cuff was increased. When checked with a laryngoscope, it was confirmed that the tube was pushed out, and the cuff was caught on the vocal cords, causing air leakage. Since inserting the tube deeply did not solve the problem, replacing it with a nasal RAE tube (Polar™, Preformed Tracheal Tube, Smith Medical, Inc., USA) did not cause air leakage; thus, we reported this case.

Autistic youth display difficulties in emotion recognition, yet little research has examined behavioral and neural indices of vocal emotion recognition (VER). The current study examines behavioral and event-related potential (N100, P200, Late Positive Potential [LPP]) indices of VER in autistic and non-autistic youth. Participants (N = 164) completed an emotion recognition task, the Diagnostic Analyses of Nonverbal Accuracy (DANVA-2) which included VER, during EEG recording. The LPP amplitude was larger in response to high intensity VER, and social cognition predicted VER errors. Verbal IQ, not autism, was related to VER errors. An interaction between VER intensity and social communication impairments revealed these impairments were related to larger LPP amplitudes during low intensity VER. Taken together, differences in VER may be due to higher order cognitive processes, not basic, early perception (N100, P200), and verbal cognitive abilities may underlie behavioral, yet occlude neural, differences in VER processing.

Vocal communication is used across extant vertebrates, is evolutionarily ancient, and been maintained, in many lineages. Here I review the neural circuit architectures that support intraspecific acoustic signaling in representative anuran, mammalian and avian species as well as two invertebrates, fruit flies and Hawaiian crickets. I focus on hindbrain motor control motifs and their ties to respiratory circuits, expression of receptors for gonadal steroids in motor, sensory, and limbic neurons as well as divergent modalities that evoke vocal responses. Hindbrain and limbic participants in acoustic communication are highly conserved, while forebrain participants have diverged between anurans and mammals, as well as songbirds and rodents. I discuss the roles of natural and sexual selection in driving speciation, as well as exaptation of circuit elements with ancestral roles in respiration, for producing sounds and driving rhythmic vocal features. Recent technical advances in whole brain fMRI across species will enable real time imaging of acoustic signaling partners, tying auditory perception to vocal production.