Deep-learning computer vision can identify increased nuchal translucency in the first trimester of pregnancy.

OBJECTIVE: Many fetal anomalies can already be diagnosed by ultrasound in the first trimester of pregnancy. Unfortunately, in clinical practice, detection rates for anomalies in early pregnancy remain low. Our aim was to use an automated image segmentation algorithm to detect one of the most common fetal anomalies: a thickened nuchal translucency (NT), which is a marker for genetic and structural anomalies. METHODS: Standardized mid-sagittal ultrasound images of the fetal head and chest were collected for 560 fetuses between 11 and 13 weeks and 6 days of gestation, 88 (15.7%) of whom had an NT thicker than 3.5 mm. Image quality was graded as high or low by two fetal medicine experts. Images were divided into a training-set (n = 451, 55 thick NT) and a test-set (n = 109, 33 thick NT). We then trained a U-Net convolutional neural network to segment the fetus and the NT region and computed the NT:fetus ratio of these regions. The ability of this ratio to separate thick (anomalous) NT regions from healthy, typical NT regions was first evaluated in ground-truth segmentation to validate the metric and then with predicted segmentation to validate our algorithm, both using the area under the receiver operator curve (AUROC). RESULTS: The ground-truth NT:fetus ratio detected thick NTs with 0.97 AUROC in both the training and test sets. The fetus and NT regions were detected with a Dice score of 0.94 in the test set. The NT:fetus ratio based on model segmentation detected thick NTs with an AUROC of 0.96 relative to clinician labels. At a 91% specificity, 94% of thick NT cases were detected (sensitivity) in the test set. The detection rate was statistically higher (p = 0.003) in high versus low-quality images (AUROC 0.98 vs. 0.90, respectively). CONCLUSION: Our model provides an explainable deep-learning method for detecting increased NT. This technique can be used to screen for other fetal anomalies in the first trimester of pregnancy.

Failure rates of nonoperative management of low-grade splenic injuries with active extravasation: an Eastern Association for the Surgery of Trauma multicenter study.

Objectives: There is little evidence guiding the management of grade I-II traumatic splenic injuries with contrast blush (CB). We aimed to analyze the failure rate of nonoperative management (NOM) of grade I-II splenic injuries with CB in hemodynamically stable patients. Methods: A multicenter, retrospective cohort study examining all grade I-II splenic injuries with CB was performed at 21 institutions from January 1, 2014, to October 31, 2019. Patients >18 years old with grade I or II splenic injury due to blunt trauma with CB on CT were included. The primary outcome was the failure of NOM requiring angioembolization/operation. We determined the failure rate of NOM for grade I versus grade II splenic injuries. We then performed bivariate comparisons of patients who failed NOM with those who did not. Results: A total of 145 patients were included. Median Injury Severity Score was 17. The combined rate of failure for grade I-II injuries was 20.0%. There was no statistical difference in failure of NOM between grade I and II injuries with CB (18.2% vs 21.1%, p>0.05). Patients who failed NOM had an increased median hospital length of stay (p=0.024) and increased need for blood transfusion (p=0.004) and massive transfusion (p=0.030). Five patients (3.4%) died and 96 (66.2%) were discharged home, with no differences between those who failed and those who did not fail NOM (both p>0.05). Conclusion: NOM of grade I-II splenic injuries with CB fails in 20% of patients. Level of evidence: IV.

Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning.

BACKGROUND: Early identification of children at risk of asthma can have significant clinical implications for effective intervention and treatment. This study aims to disentangle the relative timing and importance of early markers of asthma. METHODS: Using the CHILD Cohort Study, 132 variables measured in 1754 multi-ethnic children were included in the analysis for asthma prediction. Data up to 4 years of age was used in multiple machine learning models to predict physician-diagnosed asthma at age 5 years. Both predictive performance and variable importance was assessed in these models. RESULTS: Early-life data (≤1 year) has limited predictive ability for physician-diagnosed asthma at age 5 years (area under the precision-recall curve (AUPRC) < 0.35). The earliest reliable prediction of asthma is achieved at age 3 years, (area under the receiver-operator curve (AUROC) > 0.90) and (AUPRC > 0.80). Maternal asthma, antibiotic exposure, and lower respiratory tract infections remained highly predictive throughout childhood. Wheezing status and atopy are the most important predictors of early childhood asthma from among the factors included in this study. CONCLUSIONS: Childhood asthma is predictable from non-biological measurements from the age of 3 years, primarily using parental asthma and patient history of wheezing, atopy, antibiotic exposure, and lower respiratory tract infections. IMPACT: Machine learning models can predict physician-diagnosed asthma in early childhood (AUROC > 0.90 and AUPRC > 0.80) using ≥3 years of non-biological and non-genetic information, whereas prediction with the same patient information available before 1 year of age is challenging. Wheezing, atopy, antibiotic exposure, lower respiratory tract infections, and the child's mother having asthma were the strongest early markers of 5-year asthma diagnosis, suggesting an opportunity for earlier diagnosis and intervention and focused assessment of patients at risk for asthma, with an evolving risk stratification over time.

Early Cancer Detection in Li-Fraumeni Syndrome with Cell-Free DNA.

People with Li-Fraumeni syndrome (LFS) harbor a germline pathogenic variant in the TP53 tumor suppressor gene, face a near 100% lifetime risk of cancer, and routinely undergo intensive surveillance protocols. Liquid biopsy has become an attractive tool for a range of clinical applications, including early cancer detection. Here, we provide a proof-of-principle for a multimodal liquid biopsy assay that integrates a targeted gene panel, shallow whole-genome, and cell-free methylated DNA immunoprecipitation sequencing for the early detection of cancer in a longitudinal cohort of 89 LFS patients. Multimodal analysis increased our detection rate in patients with an active cancer diagnosis over uni-modal analysis and was able to detect cancer-associated signal(s) in carriers prior to diagnosis with conventional screening (positive predictive value = 67.6%, negative predictive value = 96.5%). Although adoption of liquid biopsy into current surveillance will require further clinical validation, this study provides a framework for individuals with LFS. SIGNIFICANCE: By utilizing an integrated cell-free DNA approach, liquid biopsy shows earlier detection of cancer in patients with LFS compared with current clinical surveillance methods such as imaging. Liquid biopsy provides improved accessibility and sensitivity, complementing current clinical surveillance methods to provide better care for these patients. See related commentary by Latham et al., p. 23. This article is featured in Selected Articles from This Issue, p. 5.

Dissecting the heterogeneity of "in the wild" stress from multimodal sensor data.

Stress is associated with numerous chronic health conditions, both mental and physical. However, the heterogeneity of these associations at the individual level is poorly understood. While data generated from individuals in their day-to-day lives "in the wild" may best represent the heterogeneity of stress, gathering these data and separating signals from noise is challenging. In this work, we report findings from a major data collection effort using Digital Health Technologies (DHTs) and frontline healthcare workers. We provide insights into stress "in the wild", by using robust methods for its identification from multimodal data and quantifying its heterogeneity. Here we analyze data from the Stress and Recovery in Frontline COVID-19 Workers study following 365 frontline healthcare workers for 4-6 months using wearable devices and smartphone app-based measures. Causal discovery is used to learn how the causal structure governing an individual's self-reported symptoms and physiological features from DHTs differs between non-stress and potential stress states. Our methods uncover robust representations of potential stress states across a population of frontline healthcare workers. These representations reveal high levels of inter- and intra-individual heterogeneity in stress. We leverage multiple stress definitions that span different modalities (from subjective to physiological) to obtain a comprehensive view of stress, as these differing definitions rarely align in time. We show that these different stress definitions can be robustly represented as changes in the underlying causal structure on and off stress for individuals. This study is an important step toward better understanding potential underlying processes generating stress in individuals.

The promises and challenges of clinical AI in community paediatric medicine.

The widespread adoption of virtual care technologies has quickly reshaped healthcare operations and delivery, particularly in the context of community medicine. In this paper, we use the virtual care landscape as a point of departure to envision the promises and challenges of artificial intelligence (AI) in healthcare. Our analysis is directed towards community care practitioners interested in learning more about how AI can change their practice along with the critical considerations required to integrate AI into their practice. We highlight examples of how AI can enable access to new sources of clinical data while augmenting clinical workflows and healthcare delivery. AI can help optimize how and when care is delivered by community practitioners while also improving practice efficiency, accessibility, and the overall quality of care. Unlike virtual care, however, AI is still missing many of the key enablers required to facilitate adoption into the community care landscape and there are challenges we must consider and resolve for AI to successfully improve healthcare delivery. We discuss several critical considerations, including data governance in the clinic setting, healthcare practitioner education, regulation of AI in healthcare, clinician reimbursement, and access to both technology and the internet.

Multiple Germline Events Contribute to Cancer Development in Patients with Li-Fraumeni Syndrome.

Li-Fraumeni syndrome (LFS) is an autosomal dominant cancer-predisposition disorder. Approximately 70% of individuals who fit the clinical definition of LFS harbor a pathogenic germline variant in the TP53 tumor suppressor gene. However, the remaining 30% of patients lack a TP53 variant and even among variant TP53 carriers, approximately 20% remain cancer-free. Understanding the variable cancer penetrance and phenotypic variability in LFS is critical to developing rational approaches to accurate, early tumor detection and risk-reduction strategies. We leveraged family-based whole-genome sequencing and DNA methylation to evaluate the germline genomes of a large, multi-institutional cohort of patients with LFS (n = 396) with variant (n = 374) or wildtype TP53 (n = 22). We identified alternative cancer-associated genetic aberrations in 8/14 wildtype TP53 carriers who developed cancer. Among variant TP53 carriers, 19/49 who developed cancer harbored a pathogenic variant in another cancer gene. Modifier variants in the WNT signaling pathway were associated with decreased cancer incidence. Furthermore, we leveraged the noncoding genome and methylome to identify inherited epimutations in genes including ASXL1, ETV6, and LEF1 that confer increased cancer risk. Using these epimutations, we built a machine learning model that can predict cancer risk in patients with LFS with an area under the receiver operator characteristic curve (AUROC) of 0.725 (0.633-0.810). Significance: Our study clarifies the genomic basis for the phenotypic variability in LFS and highlights the immense benefits of expanding genetic and epigenetic testing of patients with LFS beyond TP53. More broadly, it necessitates the dissociation of hereditary cancer syndromes as single gene disorders and emphasizes the importance of understanding these diseases in a holistic manner as opposed to through the lens of a single gene.

Screening and intervention for intimate partner violence at trauma centers and emergency departments: an evidence-based systematic review from the Eastern Association for the Surgery of Trauma.

Background: Intimate partner violence (IPV) is a serious public health issue with a substantial burden on society. Screening and intervention practices vary widely and there are no standard guidelines. Our objective was to review research on current practices for IPV prevention in emergency departments and trauma centers in the USA and provide evidenced-based recommendations. Methods: An evidence-based systematic review of the literature was conducted to address screening and intervention for IPV in adult trauma and emergency department patients. The Grading of Recommendations, Assessment, Development and Evaluations methodology was used to determine the quality of evidence. Studies were included if they addressed our prespecified population, intervention, control, and outcomes questions. Case reports, editorials, and abstracts were excluded from review. Results: Seven studies met inclusion criteria. All seven were centered around screening for IPV; none addressed interventions when abuse was identified. Screening instruments varied across studies. Although it is unclear if one tool is more accurate than others, significantly more victims were identified when screening protocols were implemented compared with non-standardized approaches to identifying IPV victims. Conclusion: Overall, there were very limited data addressing the topic of IPV screening and intervention in emergency medical settings, and the quality of the evidence was low. With likely low risk and a significant potential benefit, we conditionally recommend implementation of a screening protocol to identify victims of IPV in adults treated in the emergency department and trauma centers. Although the purpose of screening would ultimately be to provide resources for victims, no studies that assessed distinct interventions met our inclusion criteria. Therefore, we cannot make specific recommendations related to IPV interventions. PROSPERO registration number: CRD42020219517.

Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression.

Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the user's gene lists, aiding in hypothesis generation and data-driven literature review.

Systemic lupus erythematosus phenotypes formed from machine learning with a specific focus on cognitive impairment.

OBJECTIVE: To phenotype SLE based on symptom burden (disease damage, system involvement and patient reported outcomes), with a specific focus on objective and subjective cognitive function. METHODS: SLE patients ages 18-65 years underwent objective cognitive assessment using the ACR Neuropsychological Battery (ACR-NB) and data were collected on demographic and clinical variables, disease burden/activity, health-related quality of life (HRQoL), depression, anxiety, fatigue and perceived cognitive deficits. Similarity network fusion (SNF) was used to identify patient subtypes. Differences between the subtypes were evaluated using Kruskal-Wallis and χ2 tests. RESULTS: Of the 238 patients, 90% were female, with a mean age of 41 years (s.d. 12) and a disease duration of 14 years (s.d. 10) at the study visit. The SNF analysis defined two subtypes (A and B) with distinct patterns in objective and subjective cognitive function, disease burden/damage, HRQoL, anxiety and depression. Subtype A performed worst on all significantly different tests of objective cognitive function (P < 0.03) compared with subtype B. Subtype A also had greater levels of subjective cognitive function (P < 0.001), disease burden/damage (P < 0.04), HRQoL (P < 0.001) and psychiatric measures (P < 0.001) compared with subtype B. CONCLUSION: This study demonstrates the complexity of cognitive impairment (CI) in SLE and that individual, multifactorial phenotypes exist. Those with greater disease burden, from SLE-specific factors or other factors associated with chronic conditions, report poorer cognitive functioning and perform worse on objective cognitive measures. By exploring different ways of phenotyping SLE we may better define CI in SLE. Ultimately this will aid our understanding of personalized CI trajectories and identification of appropriate treatments.

Postnatal developmental trajectory of sex-biased gene expression in the mouse pituitary gland.

BACKGROUND: The pituitary gland regulates essential physiological processes such as growth, pubertal onset, stress response, metabolism, reproduction, and lactation. While sex biases in these functions and hormone production have been described, the underlying identity, temporal deployment, and cell-type specificity of sex-biased pituitary gene regulatory networks are not fully understood. METHODS: To capture sex differences in pituitary gene regulation dynamics during postnatal development, we performed 3' untranslated region sequencing and small RNA sequencing to ascertain gene and microRNA expression, respectively, across five postnatal ages (postnatal days 12, 22, 27, 32, 37) that span the pubertal transition in female and male C57BL/6J mouse pituitaries (n = 5-6 biological replicates for each sex at each age). RESULTS: We observed over 900 instances of sex-biased gene expression and 17 sex-biased microRNAs, with the majority of sex differences occurring with puberty. Using miRNA-gene target interaction databases, we identified 18 sex-biased genes that were putative targets of 5 sex-biased microRNAs. In addition, by combining our bulk RNA-seq with publicly available male and female mouse pituitary single-nuclei RNA-seq data, we obtained evidence that cell-type proportion sex differences exist prior to puberty and persist post-puberty for three major hormone-producing cell types: somatotropes, lactotropes, and gonadotropes. Finally, we identified sex-biased genes in these three pituitary cell types after accounting for cell-type proportion differences between sexes. CONCLUSION: Our study reveals the identity and postnatal developmental trajectory of sex-biased gene expression in the mouse pituitary. This work also highlights the importance of considering sex biases in cell-type composition when understanding sex differences in the processes regulated by the pituitary gland.

The silent trial - the bridge between bench-to-bedside clinical AI applications.

As more artificial intelligence (AI) applications are integrated into healthcare, there is an urgent need for standardization and quality-control measures to ensure a safe and successful transition of these novel tools into clinical practice. We describe the role of the silent trial, which evaluates an AI model on prospective patients in real-time, while the end-users (i.e., clinicians) are blinded to predictions such that they do not influence clinical decision-making. We present our experience in evaluating a previously developed AI model to predict obstructive hydronephrosis in infants using the silent trial. Although the initial model performed poorly on the silent trial dataset (AUC 0.90 to 0.50), the model was refined by exploring issues related to dataset drift, bias, feasibility, and stakeholder attitudes. Specifically, we found a shift in distribution of age, laterality of obstructed kidneys, and change in imaging format. After correction of these issues, model performance improved and remained robust across two independent silent trial datasets (AUC 0.85-0.91). Furthermore, a gap in patient knowledge on how the AI model would be used to augment their care was identified. These concerns helped inform the patient-centered design for the user-interface of the final AI model. Overall, the silent trial serves as an essential bridge between initial model development and clinical trials assessment to evaluate the safety, reliability, and feasibility of the AI model in a minimal risk environment. Future clinical AI applications should make efforts to incorporate this important step prior to embarking on a full-scale clinical trial.

Machine learning classification of multiple sclerosis in children using optical coherence tomography.

BACKGROUND: In children, multiple sclerosis (MS) is the ultimate diagnosis in only 1/5 to 1/3 of cases after a first episode of central nervous system (CNS) demyelination. As the visual pathway is frequently affected in MS and other CNS demyelinating disorders (DDs), structural retinal imaging such as optical coherence tomography (OCT) can be used to differentiate MS. OBJECTIVE: This study aimed to investigate the utility of machine learning (ML) based on OCT features to identify distinct structural retinal features in children with DDs. METHODS: This study included 512 eyes from 187 (neyes = 374) children with demyelinating diseases and 69 (neyes = 138) controls. Input features of the analysis comprised of 24 auto-segmented OCT features. RESULTS: Random Forest classifier with recursive feature elimination yielded the highest predictive values and identified DDs with 75% and MS with 80% accuracy, while multiclass distinction between MS and monophasic DD was performed with 64% accuracy. A set of eight retinal features were identified as the most important features in this classification. CONCLUSION: This study demonstrates that ML based on OCT features can be used to support a diagnosis of MS in children.

An integration engineering framework for machine learning in healthcare.

Background and Objectives: Machine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare costs. Yet only a small fraction of all Machine Learning models for health care have been successfully integrated into the clinical space. There are no current guidelines for clinical model integration, leading to waste, unnecessary costs, patient harm, and decreases in efficiency when improperly implemented. Systems engineering is widely used in industry to achieve an integrated system of systems through an interprofessional collaborative approach to system design, development, and integration. We propose a framework based on systems engineering to guide the development and integration of Machine Learning models in healthcare. Methods: Applied systems engineering, software engineering and health care Machine Learning software development practices were reviewed and critically appraised to establish an understanding of limitations and challenges within these domains. Principles of systems engineering were used to develop solutions to address the identified problems. The framework was then harmonized with the Machine Learning software development process to create a systems engineering-based Machine Learning software development approach in the healthcare domain. Results: We present an integration framework for healthcare Artificial Intelligence that considers the entirety of this system of systems. Our proposed framework utilizes a combined software and integration engineering approach and consists of four phases: (1) Inception, (2) Preparation, (3) Development, and (4) Integration. During each phase, we present specific elements for consideration in each of the three domains of integration: The Human, The Technical System, and The Environment. There are also elements that are considered in the interactions between these domains. Conclusion: Clinical models are technical systems that need to be integrated into the existing system of systems in health care. A systems engineering approach to integration ensures appropriate elements are considered at each stage of model design to facilitate model integration. Our proposed framework is based on principles of systems engineering and can serve as a guide for model development, increasing the likelihood of successful Machine Learning translation and integration.

The promise of machine learning applications in solid organ transplantation.

Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor-recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.

Regulating the Safety of Health-Related Artificial Intelligence.

This article analyzes whether Canada's present approach to regulating health-related artificial intelligence (AI) can address relevant safety-related challenges. Focusing primarily on Health Canada's regulation of medical devices with AI, it examines whether the existing regulatory approach can adequately address general safety concerns, as well as those related to algorithmic bias and challenges posed by the intersections of these concerns with privacy and security interests. It identifies several issues and proposes reforms that aim to ensure that Canadians can access beneficial AI while keeping unsafe products off Canadian markets and motivating safe, effective use of AI products for appropriate purposes and populations.

Correction: An Alternative to the Light Touch Digital Health Remote Study: The Stress and Recovery in Frontline COVID-19 Health Care Workers Study.

[This corrects the article DOI: 10.2196/32165.].

Diagnostic accuracy of imaging approaches for early tumor detection in children with Li-Fraumeni syndrome.

BACKGROUND: The Toronto protocol for cancer surveillance in children with Li-Fraumeni syndrome has been adopted worldwide. OBJECTIVE: To assess the diagnostic accuracy of the imaging used in this protocol. MATERIALS AND METHODS: We conducted a blinded retrospective review of imaging modalities in 31 pediatric patients. We compared imaging findings with the reference standards, which consisted of (1) histopathological diagnosis, (2) corresponding dedicated imaging or subsequent surveillance imaging or (3) clinical outcomes. We individually analyzed each modality's diagnostic performance for cancer detection and assessed it on a per-study basis for chest and abdominal regional whole-body MRI (n=115 each), brain MRI (n=101) and abdominal/pelvic US (n=292), and on a per-lesion basis for skeleton/soft tissues on whole-body MRI (n=140). RESULTS: Of 763 studies/lesions, approximately 80% had reference standards that identified 4 (0.7%) true-positive, 523 (85.3%) true-negative, 5 (0.8%) false-positive, 3 (0.5%) false-negative and 78 (12.7%) indeterminate results. There were 3 true-positives on whole-body MRI and 1 true-positive on brain MRI as well as 3 false-negatives on whole-body MRI. Sensitivities and specificities of tumor diagnosis using a worst-case scenario analysis were, respectively, 40.0% (95% confidence interval [CI]: 7.3%, 83.0%) and 38.2% (95% CI: 29.2%, 48.0%) for skeleton/soft tissues on whole-body MRI; sensitivity non-available and 97.8% (95% CI: 91.4%, 99.6%) for chest regional whole-body MRI; 100.0% (95% CI: 5.5%, 100.0%) and 96.8% (95% CI: 90.2%, 99.2%) for abdominal regional whole-body MRI; sensitivity non-available and 98.3% (95% CI: 95.3, 99.4) for abdominal/pelvic US; and 50.0% (95% CI: 2.7%, 97.3%) and 93.8% (95% CI: 85.6%, 97.7%) for brain MRI. CONCLUSION: Considerations for optimizing imaging protocol, defining criteria for abnormalities, developing a structured reporting system, and practicing consensus double-reading may enhance the diagnostic accuracy for tumor surveillance.