Identification of a DNA methylation episignature for recurrent constellations of embryonic malformations.

The term "recurrent constellations of embryonic malformations" (RCEM) is used to describe a number of multiple malformation associations that affect three or more body structures. The causes of these disorders are currently unknown, and no diagnostic marker has been identified. Consequently, providing a definitive diagnosis in suspected individuals is challenging. In this study, genome-wide DNA methylation analysis was conducted on DNA samples obtained from the peripheral blood of 53 individuals with RCEM characterized by clinical features recognized as VACTERL and/or oculoauriculovertebral spectrum association. We identified a common DNA methylation episignature in 40 out of the 53 individuals. Subsequently, a sensitive and specific binary classifier was developed based on the DNA methylation episignature. This classifier can facilitate the use of RCEM episignature as a diagnostic biomarker in a clinical setting. The study also investigated the functional correlation of RCEM DNA methylation relative to other genetic disorders with known episignatures, highlighting the common genomic regulatory pathways involved in the pathophysiology of RCEM.

Ultrasonographic Fetal Nuchal Translucency Measurements and Cytogenetic Outcomes.

Importance: Ultrasonographic measurement of fetal nuchal translucency is used in prenatal screening for trisomies 21 and 18 and other conditions. A cutoff of 3.5 mm or greater is commonly used to offer follow-up investigations, such as prenatal cell-free DNA (cfDNA) screening or cytogenetic testing. Recent studies showed a possible association with chromosomal anomalies for levels less than 3.5 mm, but extant evidence has limitations. Objective: To evaluate the association between different nuchal translucency measurements and cytogenetic outcomes on a population level. Design, Setting, and Participants: This population-based retrospective cohort study used data from the Better Outcomes Registry & Network, the perinatal registry for Ontario, Canada. All singleton pregnancies with an estimated date of delivery from September 1, 2016, to March 31, 2021, were included. Data were analyzed from March 17 to August 14, 2023. Exposures: Nuchal translucency measurements were identified through multiple-marker screening results. Main Outcomes and Measures: Chromosomal anomalies were identified through all Ontario laboratory-generated prenatal and postnatal cytogenetic tests. Cytogenetic testing results, supplemented with information from cfDNA screening and clinical examination at birth, were used to identify pregnancies without chromosomal anomalies. Multivariable modified Poisson regression with robust variance estimation and adjustment for gestational age was used to compare cytogenetic outcomes for pregnancies with varying nuchal translucency measurement categories and a reference group with nuchal translucency less than 2.0 mm. Results: Of 414 268 pregnancies included in the study (mean [SD] maternal age at estimated delivery date, 31.5 [4.7] years), 359 807 (86.9%) had a nuchal translucency less than 2.0 mm; the prevalence of chromosomal anomalies in this group was 0.5%. An increased risk of chromosomal anomalies was associated with increasing nuchal translucency measurements, with an adjusted risk ratio (ARR) of 20.33 (95% CI, 17.58-23.52) and adjusted risk difference (ARD) of 9.94% (95% CI, 8.49%-11.39%) for pregnancies with measurements of 3.0 to less than 3.5 mm. The ARR was 4.97 (95% CI, 3.45-7.17) and the ARD was 1.40% (95% CI, 0.77%-2.04%) when restricted to chromosomal anomalies beyond the commonly screened aneuploidies (excluding trisomies 21, 18, and 13 and sex chromosome aneuploidies). Conclusions and Relevance: In this cohort study of 414 268 singleton pregnancies, those with nuchal translucency measurements less than 2.0 mm were at the lowest risk of chromosomal anomalies. Risk increased with increasing measurements, including measurements less than 3.5 mm and anomalies not routinely screened by many prenatal genetic screening programs.

CAManim: Animating end-to-end network activation maps.

Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to image-based applications is the development of Convolutional Neural Networks (CNNs), which possess the ability to automatically extract features from data. However, comprehending these complex models and their learned representations, which typically comprise millions of parameters and numerous layers, remains a challenge for both developers and end-users. This challenge arises due to the absence of interpretable and transparent tools to make sense of black-box models. There exists a growing body of Explainable Artificial Intelligence (XAI) literature, including a collection of methods denoted Class Activation Maps (CAMs), that seek to demystify what representations the model learns from the data, how it informs a given prediction, and why it, at times, performs poorly in certain tasks. We propose a novel XAI visualization method denoted CAManim that seeks to simultaneously broaden and focus end-user understanding of CNN predictions by animating the CAM-based network activation maps through all layers, effectively depicting from end-to-end how a model progressively arrives at the final layer activation. Herein, we demonstrate that CAManim works with any CAM-based method and various CNN architectures. Beyond qualitative model assessments, we additionally propose a novel quantitative assessment that expands upon the Remove and Debias (ROAD) metric, pairing the qualitative end-to-end network visual explanations assessment with our novel quantitative "yellow brick ROAD" assessment (ybROAD). This builds upon prior research to address the increasing demand for interpretable, robust, and transparent model assessment methodology, ultimately improving an end-user's trust in a given model's predictions. Examples and source code can be found at: https://omni-ml.github.io/pytorch-grad-cam-anim/.

Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis.

Deep learning algorithms have demonstrated remarkable potential in clinical diagnostics, particularly in the field of medical imaging. In this study, we investigated the application of deep learning models in early detection of fetal kidney anomalies. To provide an enhanced interpretation of those models' predictions, we proposed an adapted two-class representation and developed a multi-class model interpretation approach for problems with more than two labels and variable hierarchical grouping of labels. Additionally, we employed the explainable AI (XAI) visualization tools Grad-CAM and HiResCAM, to gain insights into model predictions and identify reasons for misclassifications. The study dataset consisted of 969 ultrasound images from unique patients; 646 control images and 323 cases of kidney anomalies, including 259 cases of unilateral urinary tract dilation and 64 cases of unilateral multicystic dysplastic kidney. The best performing model achieved a cross-validated area under the ROC curve of 91.28% ± 0.52%, with an overall accuracy of 84.03% ± 0.76%, sensitivity of 77.39% ± 1.99%, and specificity of 87.35% ± 1.28%. Our findings emphasize the potential of deep learning models in predicting kidney anomalies from limited prenatal ultrasound imagery. The proposed adaptations in model representation and interpretation represent a novel solution to multi-class prediction problems.