3-Dimensional Facial Analysis-Facing Precision Public Health

Current and Future Advances in Surgical Therapy for Pituitary Adenoma

The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient's journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols. While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.

3D facial analysis for rare disease diagnosis and treatment monitoring: Proof-Of-Concept plan for hereditary angioedema

Rare diseases pose a diagnostic conundrum to even the most experienced clinicians around the world. The technology could play an assistive role in hastening the diagnosis process. Data-driven methodologies can identify distinctive disease features and create a definitive diagnostic spectrum. The healthcare professionals in developed and developing nations would benefit immensely from these approaches resulting in quicker diagnosis and enabling early care for the patients. Hereditary Angioedema is one such rare disease that requires a lengthy diagnostic cascade ensuing massive patient inconvenience and cost burden on the healthcare system. It is hypothesized that facial analysis with advanced imaging and algorithmic association can create an ideal diagnostic peer to the clinician while assimilating signs and symptoms in the hospital. 3D photogrammetry has been applied to diagnose rare diseases in various cohorts. The facial features are captured at a granular level in utmost finer detail. A validated and proven algorithm-powered software provides recommendations in real-time. Thus, paving the way for quick and early diagnosis to well-trained or less trained clinicians in different settings around the globe. The generated evidence indicates the strong applicability of 3 D photogrammetry in association with proprietary Cliniface software to Hereditary Angioedema for aiding in the diagnostic process. The approach, mechanism, and beneficial impact have been sketched out appropriately herein. This blueprint for hereditary angioedema may have far-reaching consequences beyond disease diagnosis to benefit all the stakeholders in the healthcare arena including research and new drug development.

Innovation in Informatics to Improve Clinical Care and Drug Accessibility for Rare Diseases in China

Background: In China, there are severe unmet medical needs of people living with rare diseases. Relatedly, there is a dearth of data to inform rare diseases policy. This is historically partially due to the lack of informatics infrastructure, including standards and terminology, data sharing mechanisms and network; and concerns over patient privacy protection.

Objective: This study aims to introduce the progress of China's rare disease informatics platform and knowledgebase, and to discuss critical enablers of rare disease informatics innovation, including: data standardization; knowledgebase construction; national policy support; and multi-stakeholder participation.

Methods: A systemic national strategy, delivered through multi-stakeholder engagement, has been implemented to create and accelerate the informatics infrastructure to support rare diseases management. This includes a disease registry system, together with more than 80 hospitals, to perform comprehensive research information collection, including clinical, genomic and bio-sample data. And a case reporting system, with a network of 324 hospitals, covering all mainland Chinese provinces, to further support reporting of rare diseases data. International standards were incorporated, and privacy issues were addressed through HIPAA compliant rules.

Results: The National Rare Diseases Registry System of China (NRDRS) now covers 166 rare diseases and more than 63,000 registered patients. The National Rare Diseases Case Reporting System of China (NRDCRS) was primarily founded on the National Network of Rare Diseases (NNRD) of 324 hospitals and focused on real-time rare diseases case reporting; more than 400,000 cases have been reported. Based on the data available in the two systems, the National Center for Health Technology Assessment (HTA) of Orphan Medicinal Products (OMP) has been established and the expert consensus on HTA of OMP was produced. The largest knowledgebase for rare disease in Chinese has also been developed.

Conclusion: A national strategy and the coordinating mechanism is the key to success in the improvement of Chinese rare disease clinical care and drug accessibility. Application of innovative informatics solutions can help accelerate the process, improve quality and increase efficiency.

Barriers and Considerations for Diagnosing Rare Diseases in Indigenous Populations

Advances in omics and specifically genomic technologies are increasingly transforming rare disease diagnosis. However, the benefits of these advances are disproportionately experienced within and between populations, with Indigenous populations frequently experiencing diagnostic and therapeutic inequities. The International Rare Disease Research Consortium (IRDiRC) multi-stakeholder partnership has been advancing toward the vision of all people living with a rare disease receiving an accurate diagnosis, care, and available therapy within 1 year of coming to medical attention. In order to further progress toward this vision, IRDiRC has created a taskforce to explore the access barriers to diagnosis of rare genetic diseases faced by Indigenous peoples, with a view of developing recommendations to overcome them. Herein, we provide an overview of the state of play of current barriers and considerations identified by the taskforce, to further stimulate awareness of these issues and the passage toward solutions. We focus on analyzing barriers to accessing genetic services, participating in genomic research, and other aspects such as concerns about data sharing, the handling of biospecimens, and the importance of capacity building.

Using Medical Big Data to Develop Personalized Medicine for Dry Eye Disease

Dry eye disease (DED) is a chronic, multifactorial ocular surface disorder with multiple etiologies that results in tear film instability. Globally, the prevalence of DED is expected to increase with an aging society and daily use of digital devices. Unfortunately, the medical field is currently unprepared to meet the medical needs of patients with DED. Noninvasive, reliable, and readily reproducible biomarkers have not yet been identified, and the current mainstay treatment for DED relies on symptom alleviation using eye drops with no effective preventative therapies available. Medical big data analyses, mining information from multiomics studies and mobile health applications, may offer a solution for managing chronic conditions such as DED. Omics-based data on individual physiologic status may be leveraged to prevent high-risk diseases, accurately diagnose illness, and improve patient prognosis. Mobile health applications enable the portable collection of real-world medical data and biosignals through personal devices. Together, these data lay a robust foundation for personalized treatments for various ocular surface diseases and other pathologies that currently lack the components of precision medicine. To fully implement personalized and precision medicine, traditional aggregate medical data should not be applied directly to individuals without adjustments for personal etiology, phenotype, presentation, and symptoms.

Ethical dangers of facial phenotyping through photography in psychiatric genomics studies

Psychiatric genomics research protocols are increasingly incorporating tools of deep phenotyping to observe and examine phenotypic abnormalities among individuals with neurodevelopmental disorders. In particular, photography and the use of two-dimensional and three-dimensional facial analysis is thought to shed further light on the phenotypic expression of the genes underlying neurodevelopmental disorders, as well as provide potential diagnostic tools for clinicians. In this paper, I argue that the research use of photography to aid facial phenotyping raises deeply fraught issues from an ethical point of view. First, the process of objectification through photographic imagery and facial analysis could potentially worsen the stigmatisation of persons with neurodevelopmental disorders. Second, the use of photography for facial phenotyping has worrying parallels with the historical misuse of photography to advance positive and negative eugenics around race, ethnicity and intellectual disability. The paper recommends ethical caution in the use of photography and facial phenotyping in psychiatric genomics studies exploring neurodevelopmental disorders, outlining certain necessary safeguards, such as a critical awareness of the history of anthropometric photography use among scientists, as well as the exploration of photographic methodologies that could potentially empower individuals with disabilities.

Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.

Optimizing Precision Medicine for Public Health

Advances in precision medicine have presented challenges to traditional public health decision-making paradigms. Historical methods of allocating healthcare funds based on safety, efficacy, and efficiency, are challenged in a healthcare delivery model that focuses on individualized variations in pathology that form the core of precision medicine. Public health policy and decision-making must adapt to this new frontier of healthcare delivery to ensure that the broad public health goals of reducing healthcare disparities and improving the health of populations are achieved, through effective and equitable allocation of healthcare funds. This paper discusses contemporary applications of precision medicine, and the potential impacts of these on public health policy and decision-making, with particular focus on patients living with rare diseases and rare cancers. The authors then reconcile these, presenting precision public health as the bridge between these seemingly competing fields.

Big Data's Role in Precision Public Health

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

Improved Diagnosis and Care for Rare Diseases through Implementation of Precision Public Health Framework

Public health relies on technologies to produce and analyse data, as well as effectively develop and implement policies and practices. An example is the public health practice of epidemiology, which relies on computational technology to monitor the health status of populations, identify disadvantaged or at risk population groups and thereby inform health policy and priority setting. Critical to achieving health improvements for the underserved population of people living with rare diseases is early diagnosis and best care. In the rare diseases field, the vast majority of diseases are caused by destructive but previously difficult to identify protein-coding gene mutations. The reduction in cost of genetic testing and advances in the clinical use of genome sequencing, data science and imaging are converging to provide more precise understandings of the 'person-time-place' triad. That is: who is affected (people); when the disease is occurring (time); and where the disease is occurring (place). Consequently we are witnessing a paradigm shift in public health policy and practice towards 'precision public health'.Patient and stakeholder engagement has informed the need for a national public health policy framework for rare diseases. The engagement approach in different countries has produced highly comparable outcomes and objectives. Knowledge and experience sharing across the international rare diseases networks and partnerships has informed the development of the Western Australian Rare Diseases Strategic Framework 2015-2018 (RD Framework) and Australian government health briefings on the need for a National plan.The RD Framework is guiding the translation of genomic and other technologies into the Western Australian health system, leading to greater precision in diagnostic pathways and care, and is an example of how a precision public health framework can improve health outcomes for the rare diseases population.Five vignettes are used to illustrate how policy decisions provide the scaffolding for translation of new genomics knowledge, and catalyze transformative change in delivery of clinical services. The vignettes presented here are from an Australian perspective and are not intended to be comprehensive, but rather to provide insights into how a new and emerging 'precision public health' paradigm can improve the experiences of patients living with rare diseases, their caregivers and families.The conclusion is that genomic public health is informed by the individual and family needs, and the population health imperatives of an early and accurate diagnosis; which is the portal to best practice care. Knowledge sharing is critical for public health policy development and improving the lives of people living with rare diseases.