Explore the vast range of titles available.

Oromandibular dystonia is a focal dystonia that manifests as involuntary masticatory and/or tongue muscle contractions. This movement disorder is frequently misdiagnosed as a temporomandibular disorder. Hence, it would be useful to establish a method that makes it possible for patients with the condition to find appropriate medical institutions by themselves. The author produced a website Involuntary movements of the stomatognathic region (https://sites. google.com/site/oromandibulardystoniaenglish/) for patients with oromandibular dystonia, which is available in twenty languages. It has been viewed more than 1,000,000 times by individuals from all over the world. The visitors to the site have completed questionnaires and/or sent images or videos of their involuntary movements over the internet. Cyberconsultations (remote diagnosis) were also performed via Skype™. Approximately 1000 patients with involuntary stomatognathic movements visited our department. Only 12.5% of the patients had previously been diagnosed with or were suspected to have dystonia. The findings of this study suggest that the multilingual website has contributed to increasing awareness of oromandibular dystonia and that the provision of basic telemedicine via the internet can aid the diagnosis and treatment of oromandibular dystonia.

The coronavirus disease 2019 (COVID-19) pandemic has sparked changes across various domains, encompassing health, commerce, education, and the economy. Given the widespread impact of COVID-19 across numerous nations, it has strained hospital resources, oxygen reserves, and healthcare personnel. Consequently, there exists an urgent necessity to exploit sophisticated technologies such as artificial intelligence and the internet of things (IoT) to monitor patients effectively. This scholarly article proposes a prototype that integrates IoT and artificial intelligence (IA) for the surveillance of COVID-19 patients within healthcare facilities. Wearable IoT devices, equipped with embedded sensors, autonomously collect vital information like oxygen levels and body temperature. Notably, oxygen saturation and heart rate serve as significant markers in COVID-19 cases. These metrics are discerned through the deep learning capabilities of the TensorFlow library. The prototype aims to augment the intelligence of IoT sensors to identify these crucial signs through a trained model. A meticulously labeled dataset comprising oxygen saturation and heart rate data is amassed. Deep neural networks are deployed to prognosticate the disease's progression. The utilization of these technologies harbors the potential for rapid advancements in healthcare, thereby mitigating risks to human life and fostering more proactive responses to health crises.

Background Oral mucosa lesions are the third most prevalent oral pathology, following caries and periodontal diseases. Teledentistry offers an effective way to manage patients with these lesions. The accuracy of remote diagnoses and consultations relies heavily on the quality of the information and photos sent to remote specialists. This study aims to evaluate the usability and reliability of a teledentistry tool for the remote diagnosis of oral lesions. Methods The cross-sectional study included both usability evaluation and reliability assessment. The teledentistry platform, \"OralMedTeledent\", facilitated synchronous and asynchronous interactions, allowing for patient consultations, remote follow-ups, and doctor-to-doctor consultations. Usability was evaluated by 5 experts using the Nielsen heuristic checklist. Reliability was assessed from August 2022 to September 2023 with 109 patients, using Cohen's kappa coefficient to measure agreement between examiners and the gold standard in diagnosing oral lesions. Results The findings revealed 66 usability issues, most of which were related to helping users recognize, diagnose, and recover from errors, as well as issues with help and documentation. Among these, 11 issues were of minor severity. The reliability test, conducted with 109 participants (57.8% female, 42.2% male) showed that the web-based teleconsultation system performed significantly well. The system demonstrated significant substantial performance (0.81 ≤ κ < 1; P  > 0.05). Conclusion Overall, the web-based teleconsultation system has proven to be reliable for the remote diagnosis of oral lesions, making it a valuable alternative during emergencies such as the COVID-19 pandemic. However, several usability issues have been identified and need to be addressed.

A mobile-based consultation service, or telehealth, can be used for remote consultations with health care professionals for screening, self-care management, and referral. In rural Bangladesh, where there is high demand for scarce male and even scarcer female doctors, remote consultations may help women seeking maternal and child health care. Aponjon is a mHealth service in Bangladesh that provides weekly voice or text messages to pregnant women, new mothers, and family members on various aspects of maternal, neonatal, and infant health. Subscribers can also access a dedicated 24*7 call center to discuss maternal, neonatal, and infant health or emergencies with medically trained doctors. The service provides advice, primary diagnoses, prescriptions, and referrals to subscriber callers. We investigated the Aponjon service to understand access, acceptability, usability, benefits, and challenges of a mobile phone-based consultation service. We conducted call log data analysis for September to November 2015 to understand how many unique subscribers accessed the service, who accessed the service, the geographical distribution of callers, and the purpose of the calls. We also conducted a qualitative exploratory substudy of eight married women and eight married men who were subscribers to and accessed the service during this time to understand their experiences. We interviewed 11 doctors from the same service who provided phone consultations to subscribers. Approximately 3894 unique subscribers accessed the service for single or multiple consultations during the study period; 68.36% (2662/3894) of subscribers were from rural households, and 53.00% (2064/3894) of calls were made by pregnant women or new mothers. Approximately 96.08% (5081/5288) calls were nonurgent, 2.69% (142/5288) semiurgent, and 1.23% (65/5288) urgent. Almost 64.7% (134/207) semiurgent or urgent calls came between 8 PM and 8 AM. Callers found the consultation service trustworthy, cost-effective, and convenient. The doctors dispelled misconceptions and promoted good health care practices, regular health check-ups, and responsible use of medicine. They helped families understand the severity of sicknesses and advised them to seek care at health facilities for semiurgent or urgent conditions. The service lacked a pro-poor policy to support talk times of subscribers from poor households and a proper referral system to help patients find the right care at the right facilities. Although a regular messaging service is constrained by a one-way communication system, this service using the same platform, gave subscribers access to an abbreviated \"consultation\" with medical doctors. The consultations provided subscribers with valued medical advice and support, although they were limited in their population reach and their integration into the wider medical system. Further research is required to understand the impact of advice and referral, cost-effectiveness, and willingness to pay for mHealth consultation services, but this research suggests that these services should be supported or even expanded.

Background Conventional diagnostic methods for dysphagia have limitations such as long wait times, radiation risks, and restricted evaluation. Therefore, voice-based diagnostic and monitoring technologies are required to overcome these limitations. Based on our hypothesis regarding the impact of weakened muscle strength and the presence of aspiration on vocal characteristics, this single-center, prospective study aimed to develop a machine-learning algorithm for predicting dysphagia status (normal, and aspiration) by analyzing postprandial voice limiting intake to 3 cc. Methods Conducted from September 2021 to February 2023 at Seoul National University Bundang Hospital, this single center, prospective cohort study included 198 participants aged 40 or older, with 128 without suspected dysphagia and 70 with dysphagia-aspiration. Voice data from participants were collected and used to develop dysphagia prediction models using the Multi-Layer Perceptron (MLP) with MobileNet V3. Male-only, female-only, and combined models were constructed using 10-fold cross-validation. Through the inference process, we established a model capable of probabilistically categorizing a new patient's voice as either normal or indicating the possibility of aspiration. Results The pre-trained models (mn40_as and mn30_as) exhibited superior performance compared to the non-pre-trained models (mn4.0 and mn3.0). Overall, the best-performing model, mn30_as, which is a pre-trained model, demonstrated an average AUC across 10 folds as follows: combined model 0.8361 (95% CI 0.7667–0.9056; max 0.9541), male model 0.8010 (95% CI 0.6589–0.9432; max 1.000), and female model 0.7572 (95% CI 0.6578–0.8567; max 0.9779). However, for the female model, a slightly higher result was observed with the mn4.0, which scored 0.7679 (95% CI 0.6426–0.8931; max 0.9722). Additionally, the other models (pre-trained; mn40_as, non-pre-trained; mn4.0 and mn3.0) also achieved performance above 0.7 in most cases, and the highest fold-level performance for most models was approximately around 0.9. The ‘mn’ in model names refers to MobileNet and the following number indicates the ‘width_mult’ parameter. Conclusions In this study, we used mel-spectrogram analysis and a MobileNetV3 model for predicting dysphagia aspiration. Our research highlights voice analysis potential in dysphagia screening, diagnosis, and monitoring, aiming for non-invasive safer, and more effective interventions. Trial registration: This study was approved by the IRB (No. B-2109-707-303) and registered on clinicaltrials.gov (ID: NCT05149976).

Background Intraoral scans (IOS) provide three-dimensional images with approximate true colors representing a possible tool in teledentistry for remote examination. The aim of the present cross-sectional validation study was, therefore, to evaluate the levels of agreement between remote diagnoses derived from IOS and diagnoses based on clinical examinations for assessing dental and periodontal conditions. Methods The test sample comprised 10 patients representing different clinical conditions. Following the acquisition of IOS (Trios, 3Shape), a full-mouth dental and periodontal examination was done and periapical radiographs were taken. Ten dentists were asked to perform dental and periodontal scorings for each of the ten patients on a tablet computer presenting the IOS. Scores included diagnosis of gingivitis/periodontitis, and evaluated presence as well as amount of plaque and calculus, and presence of teeth exhibiting gingival recession, furcation involvement, erosion, tooth wear, stain, and non-carious cervical lesion, as well as presence of decayed, filled, and crowned teeth and implants. In a second round of assessments, the periapical radiographs were provided and the dentists were able to change the scores. The time for the remote assessment was recorded. The agreement between remote and clinical scorings (reference) was then analyzed descriptively. Results The mean time for the tele assessment was 3.17 min and the additional consultation of the radiographs accounted for another 1.48 min. The sensitivity and specificity values were 0.61 and 0.39 for gingivitis and 0.67 and 0.33 for periodontitis, with no relevant changes when radiographs were provided for the diagnosis of periodontitis (0.72 and 0.28). The agreement for dichotomized dental and periodontal indices ranged between 78 and 95%. With the provision of radiographs, the remote examiners were able to detect existing filled teeth, crowned teeth, and implants, whereas the detection of decayed teeth (70%) was not improved. Conclusions The remote examination using IOS was effective in detecting dental findings, whereas periodontal conditions could not be assessed with the same accuracy. Still, remote assessment of IOS would allow a time-efficient screening and triage of patients. Improvement of the image quality of IOS may further allow to increase the accuracy of remote assessments in dentistry. According to the Swiss Regulation this investigation is not a clinical trial and therefore no registration in a WHO-registry is needed.

Abnormal movement of the head and neck is a typical symptom of Cervical Dystonia (CD). Accurate scoring on the severity scale is of great significance for treatment planning. The traditional scoring method is to use a protractor or contact sensors to calculate the angle of the movement, but this method is time-consuming, and it will interfere with the movement of the patient. In the recent outbreak of the coronavirus disease, the need for remote diagnosis and treatment of CD has become extremely urgent for clinical practice. To solve these problems, we propose a multi-view vision based CD severity scale scoring method, which detects the keypoint positions of the patient from the frontal and lateral images, and finally scores the severity scale by calculating head and neck motion angles. We compared the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) subscale scores calculated by our vision based method with the scores calculated by a neurologist trained in dyskinesia. An analysis of the correlation coefficient was then conducted. Intra-class correlation (ICC)(3,1) was used to measure absolute accuracy. Our multi-view vision based CD severity scale scoring method demonstrated sufficient validity and reliability. This low-cost and contactless method provides a new potential tool for remote diagnosis and treatment of CD.

The Internet of Medical Things (IoMT) is transforming modern healthcare systems by merging technological, economical, and social opportunities and has recently gained traction in the healthcare domain. The severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as a severe threat to public health. COVID-19 is a highly infectious virus that is spread by person-to-person contact. Therefore, minimizing physical interactions between patients and medical healthcare workers is necessary. The significance of technology and its associated potential were fully explored and proven during the outbreak of COVID-19 in all domains of human life. Healthcare systems employ all modes of technology to facilitate the increasing number of COVID-19 patients. The need for remote healthcare was reemphasized, and many remote healthcare solutions were adopted. Various IoMT-based systems were proposed and implemented to support traditional healthcare systems with reaching the maximum number of people remotely. The objective of this research is twofold. First, a systematic literature review (SLR) is conducted to critically evaluate 76 articles on IoMT systems for different medical applications, especially for COVID-19 and other health sectors. Secondly, we briefly review IoMT frameworks and the role of IoMT-based technologies in COVID-19 and propose a framework, named ‘cov-AID’, that remotely monitors and diagnoses the disease. The proposed framework encompasses the benefits of IoMT sensors and extensive data analysis and prediction. Moreover, cov-AID also helps to identify COVID-19 outbreak regions and alerts people not to visit those locations to prevent the spread of infection. The cov-AID is a promising framework for dynamic patient monitoring, patient tracking, quick disease diagnosis, remote treatment, and prevention from spreading the virus to others. We also discuss potential challenges faced in adopting and applying big data technologies to combat COVID-19.

Teledentistry enhances access to oral healthcare by enabling remote consultations, diagnosis, and patient management. This paper explores its applications, benefits, challenges, and impact on modern dentistry. A comprehensive review of existing literature and case studies was conducted to examine the effectiveness of teledentistry. Key aspects analyzed include digital imaging, AI (artificial intelligence)-assisted diagnostics, and cloud-based patient records, which facilitate early disease detection, reduce wait times, and minimize unnecessary visits. The review also highlights how teledentistry improves collaboration among dental professionals for better treatment planning. Challenges include legal barriers, data security concerns, and limited digital infrastructure. Standardized protocols and professional training are essential for effective implementation. Future advancements in AI and telecommunication technologies will further integrate teledentistry into standard practice, improving accessibility and efficiency in oral healthcare.