Technology-enabled examinations of cardiac rhythm, optic nerve, oral health, tympanic membrane, gait and coordination evaluated jointly with routine health screenings: an observational study at the 2015 Kumbh Mela in India

Tele Otology in India: Last 10 Years-A Scopic Review

Hearing loss is the commonest sensory deficit among humans, affecting 466 million people worldwide. Early detection is the key to hearing rehabilitation, as speech and language develops early in the childhood. Scarcity of human resources and infrastructure in developing countries like India leads to difficulty in implementation of hearing screening programmes. Tele otology is very fruitful methodology in providing health care facility from distant site to the doorsteps of needy individuals. The action initiated in the field of tele otology in India was reviewed at electronic databases: Pubmed, Google scholar, Medline, Cochrane library, science direct and author mapper using the keywords 'tele otology' and 'tele audiometry' in January 2021. Eligible studies were those related to tele otology and tele audiometry in India. A total of 16 articles were shortlisted for the present study. Tele hearing testing was satisfactory for the parents in regard to accessibility, testing process and counselling. Tele audiometry surveillance shows better overall follow-up compliance rate then in-person audiological surveillance. During covid 19 pandemic virtual approach to the patient through video calling and telephone calls proved handy approach, ensuring safety profile of both health care professionals and patients. Even Tele ABR conducted in tele van shows similar results as in face-to-face mode ABR. Tele otology should be considered by the service providers and policy makers while planning for hearing screening programmes for both new-born and school going children in view of its reliability, low-cost, non-invasive and portability. The village health workers (VHWs) should be well trained in assisting tele practice and internet connectivity should be well established. Tele otology looks very promising in providing health services through the ever-expanding reach of global connectivity.

Evaluation of telemedicine for new outpatient neurological consultations

Objective

The COVID-19 pandemic has broadened the use of teleneurology, how this compares with face-to-face (F2F) clinics is unclear. This study compared virtual with F2F new neurological consultations.

Methods

We retrospectively evaluated new outpatient consultations in neurology clinics in Aberdeen Royal Infirmary. We compared sociodemographic data, time to consultation, time to diagnosis, the need for reassessment and re-investigation between traditional F2F and virtual clinics using the web-based Attend Anywhere platform or telephone into patients’ own homes (or chosen location) without a trained assistant. We calculated the relative risk (RR) of the need for reassessment and re-investigation over 6-month periods by the suspected neurological diagnosis.

Results

73% of consultations were virtual (Attend Anywhere or telephone) between June and October 2020, this was almost non-existent (<0.1%) in June–October 2019. We analysed 352 F2F (June–July 2019) and 225 virtual consultations (June–July 2020). Compared with F2F clinics, virtual clinics had a longer time to diagnosis (p=0.019), were more likely to be reassessed (RR: 2.2, 95% CI: 1.5 to 3.2; p<0.0001) and re-investigated (RR: 1.50, 95% CI: 0.88 to 2.54; p=0.133), this was likelier in those aged ≥60 years. Patients with headaches and suspected seizures were less likely to need reassessment or re-investigation following virtual clinics than multiple sclerosis and neuroinflammatory disorders, spinal cord disorders and functional neurological disorders.

Conclusion

This study demonstrates that virtual clinics have higher rates of reassessment and re-investigation than F2F clinics. As virtual clinics become a potential consultation alternative, this study should instruct the selection of patients for either consultation type.

A deep-learning toolkit for visualization and interpretation of segmented medical images

Generalizability of deep-learning (DL) model performance is not well understood and uses anecdotal assumptions for increasing training data to improve segmentation of medical images. We report statistical methods for visual interpretation of DL models trained using ImageNet initialization with natural-world (T II) and supervised learning with medical images (L MI) for binary segmentation of skin cancer, prostate tumors, and kidneys. An algorithm for computation of Dice scores from union and intersections of individual output masks was developed for synergistic segmentation by T II and L MI models. Stress testing with non-Gaussian distributions of infrequent clinical labels and images showed that sparsity of natural-world and domain medical images can counterintuitively reduce type I and type II errors of DL models. A toolkit of 30 T II and L MI models, code, and visual outputs of 59,967 images is shared to identify the target and non-target medical image pixels and clinical labels to explain the performance of DL models.

The use of teledentistry in facilitating oral health for older adults: A scoping review

BACKGROUND

Teledentistry is used in many countries to provide oral health care services. However, using teledentistry to provide oral health care services for older adults is not well documented. This knowledge gap needs to be addressed, especially when accessing a dental clinic is not possible and teledentistry might be the only way for many older adults to receive oral health care services.

TYPES OF STUDIES REVIEWED

Nine databases were searched and 3,396 studies were screened using established eligibility criteria. Included studies were original research or review articles in which the intervention of interest was delivered to an older adult population (≥ 60 years) via teledentistry. The authors followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review criteria.

RESULTS

Nineteen studies were identified that met the criteria for inclusion. Only 1 study was from the United States. Seven studies had results focusing on older adult participants only, with most of those conducted in elder care facilities. The remainder consisted of studies with mixed-age populations reporting distinct results or information for older adults. The included studies used teledentistry, in both synchronous and asynchronous modes, to provide services such as diagnosis, oral hygiene promotion, assessment and referral of oral emergencies, and postintervention follow-up.

CONCLUSIONS AND PRACTICAL IMPLICATIONS

Teledentistry comprises a variety of promising apps. The authors identified and described uses, promising possibilities, and limitations of teledentistry to improve the oral health of older adults.

Digitizing the Pharma Neurons - A Technological Operation in Progress!

BACKGROUND

Digitization and automation are the buzzwords in clinical research and pharma companies are investigating heavily here. Right from drug discovery to personalized medicine, digital patients and patient engagement, there is great consideration of technology at each step.

METHODS

The published data and online information available is reviewed to give an overview of digitization in pharma, across the drug development cycle, industry collaborations and innovations. The regulatory guidelines, innovative collaborations across industry, academics and thought leadership are presented. Also included are some ideas, suggestions, way forwards while digitizing the pharma neurons, the regulatory stand, benefits and challenges.

RESULTS

The innovations range from discovering personalized medicine to conducting virtual clinical trials, and maximizing data collection from the real-world experience. To address the increasing demand for the real-world data and the needs of tech-savvy patients, the innovations are shaping up accordingly. Pharma companies are collaborating with academics and they are co-innovating the technology for example Massachusetts Institute of Technology's program. This focuses on the modernization of clinical trials, strategic use of artificial intelligence and machine learning using real-world evidence, assess the risk-benefit ratio of deploying digital analytics in medicine, and proactively identifying the solutions.

CONCLUSION

With unfolding data on the impact of science and technology amalgamation, we need shared mindset between data scientists and medical professionals to maximize the utility of enormous health and medical data. To tackle this efficiently, there is a need of cross-collaboration and education, and align with ethical and regulatory requirements. A perfect blend of industry, regulatory, and academia will ensure successful digitization of pharma neurons.

Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health

Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal disease. 1215 intraoral fluorescent images, from 284 consenting adults aged 18-90, were analyzed using a machine learning classifier that can segment periodontal inflammation. The classifier achieved an AUC of 0.677 with precision and recall of 0.271 and 0.429, respectively, indicating a learned association between disease signatures in collected images. Periodontal diseases were more prevalent among males (p=0.0012) and older subjects (p=0.0224) in the screened population. Physicians independently examined the collected images, assigning localized modified gingival indices (MGIs). MGIs and periodontal disease were then cross-correlated with responses to a medical history questionnaire, blood pressure and body mass index measurements, and optic nerve, tympanic membrane, neurological, and cardiac rhythm imaging examinations. Gingivitis and early periodontal disease were associated with subjects diagnosed with optic nerve abnormalities (p<; 0.0001) in their retinal scans. We also report significant co-occurrences of periodontal disease in subjects reporting swollen joints (p=0.0422) and a family history of eye disease (p=0.0337). These results indicate cross-correlation of poor periodontal health with systemic health outcomes and stress the importance of oral health screenings at the primary care level. Our screening process and analysis method, using images and machine learning, can be generalized for automated diagnoses and systemic health screenings for other diseases.

Mobile Health Applications for Prenatal Assessment and Monitoring

BACKGROUND

A mobile health application is an exciting, fast-paced domain that is likely to improve prenatal care.

METHODS

In this narrative review, we summarise the use of mobile health applications in this setting with a special emphasis on both the benefits of remote monitoring devices and the potential pitfalls of their use, highlighting the need for robust regulations and guidelines before their widespread introduction into prenatal care.

RESULTS

Remote monitoring devices for four areas of prenatal care are reported: (1) cardio-tocography; (2) blood glucose levels; (3) blood pressure; and (4) prenatal ultrasound. The majority of publications are pilot projects on remote consultation, education, coaching, screening, monitoring and selective booking, mostly reporting potential medical and/or economic benefits by mobile health applications over conventional care for very specific situations, indications and locations, but not always generalizable.

CONCLUSIONS

Despite the potential advantages of these devices, some caution must be taken when implementing this technology into routine daily practice. To date, the majority of published research on mobile health in the prenatal setting consists of observational studies and there is a need for high-quality randomized controlled trials to confirm the reported clinical and economic benefits as well as the safety of this technology. There is also a need for guidance and governance on the development and validation of new apps and devices and for the implementation of mobile health technology into healthcare systems in both high and low-income settings. Finally, digital communication technologies offer perspectives towards exploration and development of the very new domain of tele-pharmacology.

Artificial intelligence and machine learning in clinical development: a translational perspective

Future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, computing power to identify clinically meaningful patterns in the data using efficient artificial intelligence and machine-learning algorithms, and regulators embracing this change through new collaborations. This perspective summarizes insights, recent developments, and recommendations for infusing actionable computational evidence into clinical development and health care from academy, biotechnology industry, nonprofit foundations, regulators, and technology corporations. Analysis and learning from publically available biomedical and clinical trial data sets, real-world evidence from sensors, and health records by machine-learning architectures are discussed. Strategies for modernizing the clinical development process by integration of AI- and ML-based digital methods and secure computing technologies through recently announced regulatory pathways at the United States Food and Drug Administration are outlined. We conclude by discussing applications and impact of digital algorithmic evidence to improve medical care for patients.

Artificial Intelligence for Clinical Trial Design

Clinical trials consume the latter half of the 10 to 15 year, 1.5-2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical development costs, rendering the loss per failed clinical trial at 800 million to 1.4 billion USD. Suboptimal patient cohort selection and recruiting techniques, paired with the inability to monitor patients effectively during trials, are two of the main causes for high trial failure rates: only one of 10 compounds entering a clinical trial reaches the market. We explain how recent advances in artificial intelligence (AI) can be used to reshape key steps of clinical trial design towards increasing trial success rates.