PhenoScore: AI-based phenomics to quantify rare disease and genetic variation

While both molecular and phenotypic data are essential when interpreting genetic variants, prediction scores (CADD, PolyPhen, and SIFT) have focused on molecular details to evaluate pathogenicity — omitting phenotypic features. To unlock the full potential of phenotypic data, we developed PhenoScore: an open source, artificial intelligence-based phenomics framework. PhenoScore combines facial recognition technology with Human Phenotype Ontology (HPO) data analysis to quantify phenotypic similarity at both the level of individual patients as well as of cohorts. We prove PhenoScore’s ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 25 out of 26 investigated genetic syndromes against clinical features observed in individuals with other neurodevelopmental disorders. Moreover, PhenoScore was able to provide objective clinical evidence for two distinct ADNP-related phenotypes, that had already been established functionally, but not yet phenotypically. Hence, PhenoScore will not only be of use to unbiasedly quantify phenotypes to assist genomic variant interpretation at the individual level, such as for reclassifying variants of unknown clinical significance, but is also of importance for detailed genotype-phenotype studies

The National Dutch Breast Implant Registry: user-reported experiences and importance

Background: Robust (inter-)national breast implant registries are important. For some, registries are an administrative burden, for others they represent a solution for the discussions involving breast implants. The DBIR is one of the first national, opt-out, clinical registries of breast implants, providing information for clinical auditing and product recall. Four years after its introduction, it is time to address users’ comments in order to keep improving quality of registration, and patient safety. This study assesses users’ feedback focusing on importance of registration, logistics and user experience, and areas of improvement. Methods: In May 2018, a standardized online study–specific questionnaire was sent out to all members of the Netherlands Society of Plastic Surgery. Descriptive statistics were reported in absolute frequencies and/or percentages. Results: A total of 102 members responded to the questionnaire (response rate, 24.2%). Of all respondents, 97.1% were actively registering in DBIR. Respondents rated the importance of registration in DBIR as 8.1 out of 10 points. Ninety-one respondents suggested improvements for the DBIR. All comments were related to registration convenience and provision of automatically generated data. Conclusions: Respondents believe that registration is highly important and worth the administrative burden. However, we should collectively keep improving accuracy, usability and sustainability of breast

Defining Quality Indicators for Breast Device Surgery: Using Registries for Global Benchmarking

Background: Breast device registries monitor devices encompassing breast implants, tissue expanders and dermal matrices, and the quality of care and patient outcomes for breast device surgery. Defining a standard set of quality indicators and risk adjustment factors will enable consistency and adjustment for case-mix in benchmarking quality of care across breast implant registries. This study aimed to develop a set of quality indicators to enable assessment and reporting of quality of care for breast device surgery which can be applied globally. Methods: A scoping literature review was undertaken, and potential quality indicators were identified. Consensus on the final list of quality indicators was obtained using a modified Delphi approach. This process involved a series of online surveys, and teleconferences over 6 months. The Delphi panel included participants from various countries and representation from surgical specialty groups including breast and general surgeons, plastic and reconstructive surgeons, cosmetic surgeons, a breast-care nurse, a consumer, a devices regulator (Therapeutic Goods Administration), and a biostatistician. A total of 12 candidate indicators were proposed: Intraoperative antibiotic wash, intraoperative antiseptic wash, preoperative antibiotics, nipple shields, surgical plane, volume of implant, funnels, immediate versus delayed reconstruction, time to revision, reoperation due to complications, patient satisfaction, and volume of activity. Results: Three of the 12 proposed indicators were endorsed by the panel: preoperative intravenous antibiotics, reoperation due to complication, and patient reported outcome measures. Conclusion: The 3 endorsed quality indicator measures will enable breast device registries to standardize benchmarking of care internationally for patients undergoing breast device surgery

International lower limb collaborative (INTELLECT) study: a multicentre, international retrospective audit of lower extremity open fractures

Trauma remains a major cause of mortality and disability across the world1, with a higher burden in developing nations2. Open lower extremity injuries are devastating events from a physical3, mental health4, and socioeconomic5 standpoint. The potential sequelae, including risk of chronic infection and amputation, can lead to delayed recovery and major disability6. This international study aimed to describe global disparities, timely intervention, guideline-directed care, and economic aspects of open lower limb injuries

The JASP guidelines for conducting and reporting a Bayesian analysis

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework

Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore's ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype-phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.PhenoScore is an open-source machine-learning tool that combines facial image recognition with Human Phenotype Ontology for genetic syndrome identification without genomic data, with applications to subgroup analysis and variants of unknown significance classification.Genetics of disease, diagnosis and treatmen

Functional MRI BOLD timeseries and diffusion-weighted MRI probabilistic streamline counts

We provide resting-state functional MRI BOLD response timeseries as well as diffusion weighted MRI probabilistic streamline counts for twenty healthy subjects. Functional data is presented in the form of 1029 samples (timeseries) for 14 subcortical areas: bilateral Accumbens, Amygdala, Caudate, Hippocampus, Pallidum, Putamen and Thalamus (first 7 regions in the left hemisphere, followed by 7 regions in the right hemisphere). The data matrix is in the form #subjects x #timepoints x #areas. Structural data is presented in the form of a matrix in which each element (i,j) represents the number of probabilistic streamlines originating in region i and terminating in region j. The same regions are available as for the functional data, in the same ordering. Streamline counts from a region to itself have been discarded. More detail is provided in "Bayesian estimation of conditional independence graphs improves functional connectivity estimates", PLOSONE (forthcoming)