Audio Files Research Articles

Retraction Note: MLPD: a multi-layer protection with deduplication technique to preserve audio file transmission over the public domain

Retraction Note: MLPD: a multi-layer protection with deduplication technique to preserve audio file transmission over the public domain

"They Have to Make an Effort Too": What Decliners Can Teach Us About HIV Cure/Remission-Related Clinical Trials? Results from a French Qualitative Study.

Only one study to date has focused on people living with HIV (PLWH) who refused to participate in a HIV cure/remission-related clinical trial (HCCT)-"decliners" hereafter-that included analytical treatment interruption (ATI). Exploring why these persons refuse may provide valuable information to ensure more ethical recruitment and support in HCCTs within the bigger picture of improving HIV cure research. The qualitative component of the AMEP-EHVA-T02/ANRS-95052 study, called AMEP-Decliners, documented the experiences of French PLWH who refused to participate in EHVA-T02/ANRS-VRI07, a phase II randomized, placebo-controlled HCCT with ATI. AMEP-Decliners comprised semi-structured individual interviews with six decliners in two HIV care sites in France between September 2022 and March 2023. The interviews documented their expectations regarding HCCTs, reasons for refusal, and perceived factors that might have led them to participate. Audio files were transcribed, and an inductive thematic analysis was performed. Surprisingly, the main reason for refusal was not ATI but the trial monitoring. Besides the frequency of appointments, respondents emphasized the incompatibility with their active life. One underlying reason for refusal was that participating would have meant "break[ing] the carefree attitude about the disease," reflecting the substantial psychological burden associated with participation. Finally, respondents perceived that the trial's clinical team did not sufficiently recognize their "normal life" and the level of commitment required to participate, leading them to call for greater involvement by the team: "they have to make an effort too." Results from decliners' discourses highlighted that two levels of commitment to participation must be considered when developing HCCTs: psychological burden and logistical constraints. We suggest allowing home examinations and flexible appointment times, prioritizing face-to-face invitations in order to address the psychological burden associated with HCCT participation, and explaining the reasons for monitoring constraints when they cannot be alleviated. Further studies are necessary to confirm our results.

An invisible knife to add to your toolkit: a narrative review of how podcasts have audibly shaped, styled, and sharpened surgical education.

Podcasts as a mobile learning platform to augment surgical expertise and dialogue have been embraced by many surgical educators and learners. Podcasts are digital audio files that can be streamed or downloaded from the internet to individual devices. Surgical podcast literature was appraised with a search using the keywords 'surgery' AND 'podcast' in the PubMed database which found 639 results with titles and abstracts screened to identify and review 28 relevant articles. This narrative review explores podcast origins, aims, structure and availability for diverse surgical audiences. It also appraises quality, strengths and limitations, highlighting optimal surgical podcast features. Finally, it proposes how to better implement podcasts into surgical training and clinical practice.

Emotion Classification from Speech Waveform Using Machine Learning and Deep Learning Techniques

Emotions play a key role in determining the human mental state and indirectly express an individual’s well- being. A speech emotion recognition system can extract a person’s emotions from his/her speech inputs. There are some universal emotions such as anger, disgust, fear, happiness, pleasantness, sadness and neutral. These emotions are of significance especially in a situation like the Covid pandemic, when the aged or sick are vulnerable to depression. In the current paper, we examined various classification models with finite computational strength and resources in order to determine the emotion of a person from his/her speech. Speech prosodic features like pitch, loudness, and tone of speech, and work spectral features such as Mel Frequency Capstral Coefficients (MFCCs) of the voice were used to analyze the emotions of a person. Although sequence to sequence state of the art models for speech detection that offer high levels of accuracy and precision are currently in use, the computational needs of such approaches are high and inefficient. Therefore, in this work, we emphasised analysis and comparison of different classification algorithms such as multi layer perceptron, decision tree, support vector machine, and deep neural networks such as convolutional neural network and long short term memory. Given an audio file, the emotions that were exhibited by the speaker were recognized using machine learning and deep learning techniques. A comparative study was performed to identify the most appropriate algorithms that could be used to recognize emotions. Based on the experiment results, the MLP classifier and convolutional neural network model offered better accuracy with smaller variations when compared with other models used for the study.

Enhancing Speech-to-Text and Translation Capabilities for Developing Arabic Learning Games: Integration of Whisper OpenAI Model and Google API Translate

This study tackles language barriers in computer-mediated communication by developing an application that integrates OpenAI’s Whisper ASR model and Google Translate machine translation to enable real-time, continuous speech transcription and translation and the processing of video and audio files. The application was developed using the Experimental method, incorporating standards for testing and evaluation. The integration expanded language coverage to 133 languages and improved translation accuracy. Efficiency was enhanced through the use of greedy parameters and the Faster Whisper model. Usability evaluations, based on questionnaires, revealed that the application is efficient, effective, and user-friendly, though minor issues in user satisfaction were noted. Overall, the Speech Translate application shows potential in facilitating transcription and translation for video content, especially for language learners and individuals with disabilities. Additionally, this study introduces an Arabic learning game incorporating an Artificial Neural Network using the CNN algorithm. Focusing on the “Speaking” skill, the game applies to voice and image extraction techniques, achieving a high accuracy rate of 95.52%. This game offers an engaging and interactive method for learning Arabic, a language often considered challenging. The incorporation of Artificial Neural Network technology enhances the effectiveness of the learning game, providing users with a unique and innovative language learning experience. By combining voice and image extraction techniques, the game offers a comprehensive approach to enjoyably improving Arabic speaking skills.

<span><strong>Diverse groupings in teaching English-speaking skills</strong></span>

This study aims to investigate the effects of diverse groupings on learners' English-speaking skills. Accordingly, diverse groupings as an intervention procedure were implemented over an English-speaking course. This course lasted 15 weeks and 48 EFL students from a university in Vietnam enrolled in the course. On the completion of the course, quantitative data were collected via a questionnaire surveying the involved students' evaluations of the effects of the intervention procedure. Meanwhile, participants' reflections on the course recorded in audio files served as qualitative data for the study. The obtained results from both quantitative and qualitative data reveal that participants highly valued the course because it facilitated their English speaking skills as well as cognitive and social skills. These positive effects were derived from practicing English speaking with various partners. In addition, qualitative data recorded their perspectives on different preferences for partners in group work. These patterns include partners’ English proficiency levels, sex, partnership selection, and working duration. On these results, pedagogical implications are addressed. Further expanded research in this regard within Vietnam and beyond is also suggested to overcome the present study’s limitations.

Research of the impact of noise reduction methods on the quality of audio signal recovery

The subject of the study is the analysis of various filtering algorithms for the quality of the resulting audio files. The importance of audio line filtering has grown significantly in recent years due to its key role in a variety of applications such as speech reduction and artificial intelligence. Taking into account the growing demand for solving problems related to speech recognition, the processing of audio series becomes important for determining the accuracy and efficiency of the obtained solution.The purpose of the work is to study the impact of noise suppression methods on the quality of restoration of an audio signal, which was alternately noisy with one of five types of noise - white, pink, brown, impulse, Gaussian with different power. To achieve the goal, the following tasks were solved: an analysis of the types of noise was carried out and analysis of noise reduction and filtering methods. A generalized model of noise reduction and filtering was developed, and an experiment was planned depending on the type and power of noise. Simulation of the experiment was performed by comparing the parameters of the signal-to-noise ratio before and after the experiment and the peak signal-to-noise ratio in the processed file. The following methods are used: spectral subtraction, filtering based on frequency filters and wavelet transformation.The following results were obtained: depending on the selected noises and algorithms, it was possible to achieve the lowest value of the peak signal-to-noise ratio of 21.52db, and the signal-to-noise ratio increased, which allowed further work with these audio files. The practical significance of this work is the increase in the number of available audio files for further work.Conclusions: the analysis of the obtained results showed that filtering based on frequency filters only worsened the output signal, that is, not only noise, but also useful information is filtered. In all runs, the SNR deteriorates to - 18dB. which is worse than no filtering. Algorithms of spectral subtraction and wavelet transformation improved SNR parameters and output audio files noisy with the most powerful noises in the range of 20dB, which can be considered acceptable for further processing. The results highlight the importance of using denoising and filtering for complex audio processing tasks, particularly neural network training tasks.

Image Steganography Approaches and Their Detection Strategies: A Survey

Steganography is the art and science of hidden (or covered) communication. In digital steganography, the bits of image, video, audio and text files are tweaked to represent the information to hide. This article covers the current methods for hiding information in images, alongside steganalysis methods that aim to detect the presence of steganography. By reviewing 456 references, this article discusses the different approaches that can be taken toward steganography and its much less widely studied counterpart. Currently in research older steganography approaches are more widely used than newer methods even though these show greater potential. New methods do have flaws; therefore, more research is needed to make these practically applicable. For steganalysis one of the greatest challenges is the generalisability. Often one scheme can detect the presence of one specific hiding method. More research is needed to combine current schemes and/or create new generalisable schemes. To allow readers to compare results between different papers in our work, performance indications of all steganalysis methods are outlined and a comparison of performance is included. This comparison is given using ‘topological sorting’ graphs, which compares detection results from all papers (as stated in the papers themselves) on different steganographic schemes.

Speech recognition technology for assessing team debriefing communication and interaction patterns: An algorithmic toolkit for healthcare simulation educators

BackgroundDebriefings are central to effective learning in simulation-based medical education. However, educators often face challenges when conducting debriefings, which are further compounded by the lack of empirically derived knowledge on optimal debriefing processes. The goal of this study was to explore the technical feasibility of audio-based speaker diarization for automatically, objectively, and reliably measuring debriefing interaction patterns among debriefers and participants. Additionally, it aimed to investigate the ability to automatically create statistical analyses and visualizations, such as sociograms, solely from the audio recordings of debriefings among debriefers and participants.MethodsWe used a microphone to record the audio of debriefings conducted during simulation-based team training with third-year medical students. The debriefings were led by two healthcare simulation instructors. We processed the recorded audio file using speaker diarization machine learning algorithms and validated the results manually to showcase its accuracy. We selected two debriefings to compare the speaker diarization results between different sessions, aiming to demonstrate similarities and differences in interaction patterns.ResultsTen debriefings were analyzed, each lasting about 30 min. After data processing, the recorded data enabled speaker diarization, which in turn facilitated the automatic creation of visualized interaction patterns, such as sociograms. The findings and data visualizations demonstrated the technical feasibility of implementing audio-based visualizations of interaction patterns, with an average accuracy of 97.78%.We further analyzed two different debriefing cases to uncover similarities and differences between the sessions. By quantifying the response rate from participants, we were able to determine and quantify the level of interaction patterns triggered by instructors in each debriefing session. In one session, the debriefers triggered 28% of the feedback from students, while in the other session, this percentage increased to 36%.ConclusionOur results indicate that speaker diarization technology can be applied accurately and automatically to provide visualizations of debriefing interactions. This application can be beneficial for the development of simulation educator faculty. These visualizations can support instructors in facilitating and assessing debriefing sessions, ultimately enhancing learning outcomes in simulation-based healthcare education.

An Easily Customizable Approach for Automated Species-Specific Detection of Anuran Calls Using the European Green Toad as an Example

Machine learning approaches for pattern recognition are increasingly popular. However, the underlying algorithms are often not open source, may require substantial data for model training, and are not geared toward specific tasks. We used open-source software to build a green toad breeding call detection algorithm that will aid in field data analysis. We provide instructions on how to reproduce our approach for other animal sounds and research questions. Our approach using 34 green toad call sequences and 166 audio files without green toad sounds had an accuracy of 0.99 when split into training (70%) and testing (30%) datasets. The final algorithm was applied to amphibian sounds newly collected by citizen scientists. Our function used three categories: “Green toad(s) detected”, “No green toad(s) detected”, and “Double check”. Ninety percent of files containing green toad calls were classified as “Green toad(s) detected”, and the remaining 10% as “Double check”. Eighty-nine percent of files not containing green toad calls were classified as “No green toad(s) detected”, and the remaining 11% as “Double check”. Hence, none of the files were classified in the wrong category. We conclude that it is feasible for researchers to build their own efficient pattern recognition algorithm.

A sound recognition algorithm to filter non construction site noise

Sixense Engineering have developed a new algorithm to correct the noise levels measured around construction sites, by identifying and filtering out sounds that are generated by activities outside the site (emergency services sirens, traffic horns, etc.). The idea is to go beyond the simple classification of audio files by applying the classification algorithm to all the noise events that contribute to the noise dose LAeq,T. In this innovative process, the noise dose is calculated from elementary LAeq,1min,. When noise events are detected (in terms of LAeq,1min), the corresponding audio recording (.wav file) are analysed by the sound recognition algorithm. When the algorithm identifies that the noise source is not construction related, with a high degree of confidence, the corresponding LAeq,1min is excluded from the noise dose calculation. In other words, the measured noise dose can be filtered by removing non construction related noise events produced by external sources like sirens, horns, motorbikes, birds, music, etc.

Enhancing human computer interaction with coot optimization and deep learning for multi language identification

Human-Computer Interaction (HCI) is a multidisciplinary field focused on designing and utilizing computer technology, underlining the interaction interface between computers and humans. HCI aims to generate systems that allow consumers to relate to computers effectively, efficiently, and pleasantly. Multiple Spoken Language Identification (SLI) for HCI (MSLI for HCI) denotes the ability of a computer system to recognize and distinguish various spoken languages to enable more complete and handy interactions among consumers and technology. SLI utilizing deep learning (DL) involves using artificial neural networks (ANNs), a subset of DL models, to automatically detect and recognize the language spoken in an audio signal. DL techniques, particularly neural networks (NNs), have succeeded in various pattern detection tasks, including speech and language processing. This paper develops a novel Coot Optimizer Algorithm with a DL-Driven Multiple SLI and Detection (COADL-MSLID) technique for HCI applications. The COADL-MSLID approach aims to detect multiple spoken languages from the input audio regardless of gender, speaking style, and age. In the COADL-MSLID technique, the audio files are transformed into spectrogram images as a primary step. Besides, the COADL-MSLID technique employs the SqueezeNet model to produce feature vectors, and the COA is applied to the hyperparameter range of the SqueezeNet method. The COADL-MSLID technique exploits the SLID process’s convolutional autoencoder (CAE) model. To underline the importance of the COADL-MSLID technique, a series of experiments were conducted on the benchmark dataset. The experimentation validation of the COADL-MSLID technique exhibits a greater accuracy result of 98.33% over other techniques.

APPLICATION OF MACHINE LEARNING TECHNIQUES TO INCREASE THE LEVEL OF ACCURACY OF OPTICAL CHARACTER RECOGNITION RESULTS

One of the most pervasive processes of modernity is undoubtedly digitalization, which has encompassed all key spheres of human life. The development of information technology has contributed to large-scale changes not only in the everyday aspect of life, but also more globally, automating complex business processes in the field of entrepreneurship, economics, and healthcare. The transition to digital data and documentation has provided greater accessibility to necessary information and has also enhanced the efficiency of its analysis and processing. Due to this fact, optical character recognition (OCR) technology has gained significant importance, enabling the identification and extraction of textual data from images. OCR systems play a pivotal role in the digital transformation of society as they eliminate the need for manual handling of textual information in images and are applicable in automating the majority of business processes associated with paper-based data processing, such as gathering statistical data from paper forms, reflecting paper documents in electronic document management systems, converting textual information into audio files, and so on. This paper is dedicated to describing optical character recognition technology, as well as providing an overview of machine learning techniques that are actively used in the context of its modern implementation, in order to enhance the quality of the obtained results. In addition, the paper presents the principles of operation of the described approaches, their capabilities, as well as some limitations that may be encountered when using them in various scenarios.

A study on acute expectation effects on affective state and intention after a brief full-body workout in a student sample

Introduction & Purpose Research shows that even short exercise programs can have positive effects on affective state (Focht, 2009), which in turn could have a positive influence on the intention to exercise in the future (Finne et al., 2022). The state of research in recent years also indicates that such benefits can be enhanced by manipulating expectations (Helfer et al., 2015). However, most of these studies on expectation effects were conducted in laboratory settings, meaning that the results are transferable to a natural context or everyday situations only to a limited extent. The aim of this study was therefore to examine whether the manipulation of affective expectations can influence affective state after physical activity in a more naturalistic setting; in addition, it was investigated whether the expectation manipulation has an effect on the intention for future exercise. Methods In an online experiment, 121 physically active students (25.58 ± 5.11 years), 70.2% female, were randomly assigned to either a ten-minute, virtually-guided whole-body workout (PA), a ten-minute, virtually-guided whole-body workout after manipulation of affective expectations via audio file (PA + EM), or a sedentary control intervention (CG). In the audio file, a purported speaker for the research team highlighted that recent research indicates that brief workouts would have particularly positive effects on stress experience and affective state. Pre- and post-intervention data, including affective state and intention, were collected using questionnaires, such as the Mood Survey Scales by Abele-Brehm and Brehm (1986). Results After controlling for the baseline level before the intervention via analyses of covariance (ANCOVAs), PA and PA + EM showed significantly more positive scores than CG in several parameters of affective state (e.g., scale Activation, Elation, Fatigue) after the intervention, in some scales (e.g., Affective Valence, Depression) only PA + EM differed significantly from CG. The mean scores in intention after the intervention did not differ significantly between PA + EM (27.00 ± 4.57), PA (27.41 ± 6.79), and CG (27.70 ± 5.78), p = .836. Discussion Even a short, virtually-guided workout can have strong positive effects on affective state. No direct effects of the expectation manipulation on affective state or intention were found. Possible reasons for this include ceiling effects in the participants’ expectations, or that the form of expectation manipulation used was less suitable for influencing the outcomes. However, in some affective parameters, the expectation manipulation was the decisive factor for the differences to the control group, so that more latent effects of the manipulation cannot be ruled out. Conclusion Even though no significant effects of the expectation manipulation were found, the results do not appear to be entirely unaffected by it. Also, because the differences found in affect could have a long-term effect on physical activity behavior, further research is needed in this field in order to be able to make more reliable statements. From a practical perspective, when designing or implementing interventions to increase physically active behavior, practitioners should be aware of the importance of the wording used when describing physical activity to their clients. References Abele-Brehm, A., & Brehm, W. (1986). Zur Konzeptualisierung und Messung von Befindlichkeit: Die Entwicklung der „Befindlichkeitsskalen“ (BFS) [The conceptualization and measurement of mood: The development of the “Mood Survey“]. Diagnostica, 32(3), 209-228. Finne, E., Nigg, C., Weyland, S., Sauzet, O., Wienke, B., & Jekauc, D. (2022). Examining the role of affective states in relation to exercise intentions and participation in extra-curricular exercise classes at university: A repeated measurement observational study. Frontiers in Psychol, 13, Article 815466. https://doi.org/10.3389/fpsyg.2022.815466 Focht, B. C. (2009). Brief walks in outdoor and laboratory environments: Effects on affective responses, enjoyment, and intentions to walk for exercise. Research Quarterly for Exercise and Sport, 80(3), 611-620. https://doi.org/10.1080/02701367.2009.10599600 Helfer, S. G., Elhai, J. D., & Geers, A. L. (2015). Affect and exercise: Positive affective expectations can increase post-exercise mood and exercise intentions. Annals of Behavioral Medicine, 49(2), 269-279. https://doi.org/10.1007/s12160-014-9656-1

Qualitative assessment of the enablers and barriers of COVID-19 vaccination integration, into routine immunization Programme at primary health care facilities in Ekiti state, Nigeria

BackgroundThe introduction of the COVID-19 vaccine influenced discussions to explore integrating COVID-19 vaccination into routine immunization (RI) programs. Subsequently, the WHO Strategic Advisory Group Experts (SAGE) on immunization plausibly advised countries to leverage the COVID-19 vaccination rollout as a transformative opportunity to establish a resilient immunization system, through an integrated immunization system, resulting in strengthened primary healthcare services.ObjectiveThis study assessed the enablers and barriers to integrating COVID-19 vaccination into the routine immunization programme in Ekiti state, Nigeria.MethodsThis study utilized a qualitative research approach to obtain information from 40 participants selected purposively across the primary healthcare facilities, LGA, and state levels. A three-tier approach was employed to select the LGAs. At the same time, In-depth interviews were conducted to elicit relevant information on the assessment of enablers and barriers to integrating COVID-19 vaccination into routine immunization through an interview guide. All the audio interview files were transcribed into English Language, coded, and presented using a thematic approach.ResultsA total of 40 healthcare workers across Ekiti state were selected to participate in this study. Our study found the enablers of integrating COVID-19 into routine immunization to include demand generation and social mobilization, health education, health workers training, data validation, financing, and supervision. However, rumors, demand for incentives, Adverse Events Following Immunization (AEFI), manpower shortage, poor internet network services, and bad road networks were reported as barriers to integrating COVID-19 vaccination into routine immunization in Ekiti state.ConclusionWhile the enablers of integrating COVID-19 vaccination into routine immunization may outweigh its barriers, it is pertinent to know that the efforts towards integrating COVID–19 vaccination in the RI Program is a call to all stakeholders to ensure sustainability. The identified barriers in the study must be addressed to ensure a strengthened health system. Therefore, achieving a successful integration and uptake of vaccination/immunization is anchored on driving demand generation, social mobilization, health education, availability of healthcare workers, integrated supervision, and financing.

National Institute of Standard Technology Approach for Steganography Detection on WhatsApp Audio Files

National Institute of Standard Technology Approach for Steganography Detection on WhatsApp Audio Files

Anxiety of Patients Undergoing Magnetic Resonance Imaging (MRI): The Effectiveness of Guided Mental Imagery.

Since magnetic resonance imaging (MRI) is an extensively used and fundamental diagnostic imaging method and anxiety is one of the most important confounding factors in its performance, using guided imagery is recommended. This study aimed to assess the effectiveness of guided imagery on the anxiety of patients undergoing MRI in 2023. 88 patients were randomly assigned to intervention and control groups. The intervention group listened to the nature-based guided imagery audio file during their scan, and the control group did not receive any intervention. Data were collected using demographic information and the Spielberger Anxiety Questionnaire before and after the scan. There was no significant difference between the 2 groups before the intervention regarding demographic data and anxiety. In the intervention group, the mean anxiety decreased from 104.0 ± 14.6 to 92.4 ± 9.0, showing a significant reduction in the level of anxiety in both subscales (state and trait) and the total score (P < 0.001), compared with the control group and before the intervention. The results showed that using guided imagery could decrease anxiety levels in patients undergoing MRI. Since patients' anxiety is one of the most important nursing diagnoses, performing cognitive methods, including guided imagery, as a simple, safe, inexpensive, and effective intervention should be considered.

A post‐processing framework for assessing BirdNET identification accuracy and community composition

Passively collected acoustic data have become increasingly common in wildlife research and have prompted the development of machine‐learning approaches to extract and classify large sets of audio files. BirdNET is an open‐source automatic prediction model that is popular because of its lack of training requirements for end users. Several studies have sought to test the accuracy of BirdNET and illustrate its potential in occupancy modelling of single or multiple species. However, these techniques either require extensive statistical knowledge or computational power to be applied to large datasets. In addition, there is a lack of comparisons of occupancy and community composition calculated using BirdNET and typical field methods. Here we develop a framework for assessing the accuracy of BirdNET using generalized linear mixed models to determine species‐specific confidence score thresholds. We then compare community composition under our model and another post‐processing approach to field data collected from co‐located point count surveys in northeastern Vermont. Our framework outperformed the other post‐processing method and resulted in species composition similar to that of point count surveys. Our work highlights the potential mismatch between accuracy and confidence score and the importance of developing species‐specific thresholds. The framework can facilitate research on large acoustic datasets and can be applied to output from BirdNET or other automatic prediction models.

Thoughts and opinions about fertility preservation and family building from the transgender community—an interview-based approach

BackgroundWhen considering the significant prevalence of transgender and gender nonconforming individuals, it is imperative that physicians work to understand the unique needs of this population including paths to family building and fertility preservation. ObjectiveTo understand the thoughts and opinions about fertility preservation and family building within the transgender community. Study designSurvey-based study and qualitative study at a single, large hospital-affiliated fertility center. A 16-question survey followed by a 30-minute virtual interview was utilized. Forty-three participants completed the survey and were used for quantitative analysis, and 40 audio files were used for qualitative analysis. ResultsThe average age of participants was 27.0±7.3 years old. The majority of participants were assigned female at birth (70.7%); however, most participants’ gender identities were transgender (54.8%) or nonbinary (47.6%). The average age at which participants realized they were transgender and disclosed their identity to a friend, or partner, was 16.2±7.0 (range 3–30) and 20.1±5.6 (range 11–33), respectively. The average age that participants disclosed their gender identity to their parents, siblings, or other family was 22.4±5.9 (range 12–35). Five categories were utilized to summarize the experiences and opinions of the participants: family building and parenthood goals, influences of family background/partner on parenthood goals, awareness and knowledge about fertility preservation, barrier to family building, and recommended healthcare provider communication and education. Sixteen participants (40.0%, n/N=16/40) were interested in having children in the future, 18 (45.0%, n/N=18/40) were unsure/dependent on their partner, and 6 (15.0%, n/N=6/40) were not interested in having children. Of those interested in having children (n/N=16/40), the majority wished to utilize adoption to build their family (68.8%, n/N=11/16). Nine participants (22.5%, n/N=9/40) reported that fertility preservation was never discussed. Most participants agreed that gender dysphoria, cost, inadequate counseling, and improvements in healthcare knowledge about transgender individuals were key influences on their opinions about pursuing fertility preservation or parenthood. ConclusionTransgender individuals want to build families, and the majority consider adoption the preferred method. There are still transgender individuals who are not counseled about fertility preservation. It is imperative the healthcare system continues to improve the education of healthcare providers about management and care of transgender individuals to provide the best care for this vulnerable population.

Diagnostic Accuracy of AI Algorithms in Aortic Stenosis Screening: A Systematic Review and Meta-Analysis.

Background: Aortic stenosis (AS) is frequently identified at an advanced stage after clinical symptoms appear. The aim of this systematic review and meta-analysis is to evaluate the diagnostic accuracy of artificial intelligence (AI) algorithms for AS screening.Methods: We conducted a thorough search of six databases. Several evaluation parameters, such as sensitivity, specificity, diagnostic odds ratio (DOR), negative likelihood ratio (NLR), positive likelihood ratio (PLR), and area under the curve (AUC) value were employed in the diagnostic meta-analysis of AI-based algorithms for AS screening. The AI algorithms utilized diverse data sources including electrocardiograms (ECG), chest radiographs, auscultation audio files, electronic stethoscope recordings, and cardio-mechanical signals from non-invasive wearable inertial sensors.Results: Of the 295 articles identified, 10 studies met the inclusion criteria. The pooled estimates for AI-based algorithms in diagnosing AS were as follows: sensitivity 0.83 (95% CI: 0.81-0.85), specificity 0.81 (95% CI: 0.79-0.84), PLR 4.78 (95% CI: 3.12-7.32), NLR 0.20 (95% CI: 0.13-0.28), and DOR 27.11 (95% CI: 14.40-51.05). The AUC value was 0.909 (95% CI: 0.889-0.929), indicating outstanding diagnostic accuracy. Subgroup and meta-regression analyses showed that continent, type of AS, data source, and type of AI-based method constituted sources of heterogeneity. Furthermore, we demonstrated proof of publication bias for DOR values analyzed using Egger's regression test (P = 0.002) and a funnel plot.Conclusion: Deep learning approaches represent highly sensitive, feasible, and scalable strategies to identify patients with moderate or severe AS.