HistoEM: A Pathologist-Guided and Explainable Workflow Using Histogram Embedding for Gland Classification.

Pathologists have, over several decades, developed criteria for diagnosing and grading prostate cancer. However, this knowledge has not, so far, been included in the design of convolutional neural networks (CNN) for prostate cancer detection and grading. Further, it is not known whether the features learned by machine-learning algorithms coincide with diagnostic features used by pathologists. We propose a framework that enforces algorithms to learn the cellular and subcellular differences between benign and cancerous prostate glands in digital slides from hematoxylin and eosin-stained tissue sections. After accurate gland segmentation and exclusion of the stroma, the central component of the pipeline, named HistoEM, utilizes a histogram embedding of features from the latent space of the CNN encoder. Each gland is represented by 128 feature-wise histograms that provide the input into a second network for benign vs cancer classification of the whole gland. Cancer glands are further processed by a U-Net structured network to separate low-grade from high-grade cancer. Our model demonstrates similar performance compared with other state-of-the-art prostate cancer grading models with gland-level resolution. To understand the features learned by HistoEM, we first rank features based on the distance between benign and cancer histograms and visualize the tissue origins of the 2 most important features. A heatmap of pixel activation by each feature is generated using Grad-CAM and overlaid on nuclear segmentation outlines. We conclude that HistoEM, similar to pathologists, uses nuclear features for the detection of prostate cancer. Altogether, this novel approach can be broadly deployed to visualize computer-learned features in histopathology images.

A Longitudinal Analysis of Pre- and Post-Operative Dysmorphology in Metopic Craniosynostosis.

OBJECTIVE: The purpose of this study is to objectively quantify the degree of overcorrection in our current practice and to evaluate longitudinal morphological changes using CranioRateTM, a novel machine learning skull morphology assessment tool.  . DESIGN: Retrospective cohort study across multiple time points. SETTING: Tertiary care children's hospital. PATIENTS: Patients with preoperative and postoperative CT scans who underwent fronto-orbital advancement (FOA) for metopic craniosynostosis. MAIN OUTCOME MEASURES: We evaluated preoperative, postoperative, and two-year follow-up skull morphology using CranioRateTM to generate a Metopic Severity Score (MSS), a measure of degree of metopic dysmorphology, and Cranial Morphology Deviation (CMD) score, a measure of deviation from normal skull morphology. RESULTS: Fifty-five patients were included, average age at surgery was 1.3 years. Sixteen patients underwent follow-up CT imaging at an average of 3.1 years. Preoperative MSS was 6.3 ± 2.5 (CMD 199.0 ± 39.1), immediate postoperative MSS was -2.0 ± 1.9 (CMD 208.0 ± 27.1), and longitudinal MSS was 1.3 ± 1.1 (CMD 179.8 ± 28.1). MSS approached normal at two-year follow-up (defined as MSS = 0). There was a significant relationship between preoperative MSS and follow-up MSS (R2 = 0.70). CONCLUSIONS: MSS quantifies overcorrection and normalization of head shape, as patients with negative values were less "metopic" than normal postoperatively and approached 0 at 2-year follow-up. CMD worsened postoperatively due to postoperative bony changes associated with surgical displacements following FOA. All patients had similar postoperative metopic dysmorphology, with no significant association with preoperative severity. More severe patients had worse longitudinal dysmorphology, reinforcing that regression to the metopic shape is a postoperative risk which increases with preoperative severity.

Machine Learning in Metopic Craniosynostosis: Does Phenotypic Severity Predict Long-Term Esthetic Outcome?

BACKGROUND: There have been few longitudinal studies assessing the effect of preoperative phenotypic severity on long-term esthetic outcomes in metopic craniosynostosis. This study evaluates the relationship between metopic severity and long-term esthetic outcomes using interfrontal angle (IFA) and CranioRate, a novel metopic synostosis severity measure. METHODS: Patients with metopic craniosynostosis who underwent bifrontal orbital advancement and remodeling between 2012 and 2017 were reviewed. Preoperative computed tomography head scans were analyzed for IFA and CranioRate, a machine learning algorithm which generates quantitative severity ratings including metopic severity score (MSS) and cranial morphology deviation (CMD). Long-term esthetic outcomes were assessed by craniofacial surgeons using blinded 3-rater esthetic grading of clinical photos. Raters assessed Whitaker score and the presence of temporal hollowing, lateral orbital retrusion, frontal bone irregularities and/or "any visible irregularities." RESULTS: Preoperative scans were performed at a mean age of 7.7±3.4 months, with average MSS of 6/10, CMD of 200/300, and IFA of 116.8±13.8 degrees. Patients underwent bifrontal orbital advancement and remodeling at mean 9.9±3.1 months. The average time from operation to esthetic assessment was 5.4±1.0 years. Pearson correlation revealed a significant negative correlation between MSS and age at computed tomography ( r =-0.451, P =0.004) and IFA ( r =-0.371, P =0.034) and between IFA and age at surgery ( r =-0.383, P =0.018). In multinomial logistic regression, preoperative MSS was the only independent predictor of visible irregularities (odds ratio=2.18, B =0.780, P =0.024) and preoperative IFA alone significantly predicted Whitaker score, with more acute IFA predicting worse Whitaker score (odds ratio=0.928, B =-0.074, P =0.928). CONCLUSIONS: More severe preoperative phenotypes of metopic craniosynostosis were associated with worse esthetic dysmorphology. Objective measures of preoperative metopic severity predicted long-term esthetic outcomes.

3D Photography to Quantify the Severity of Metopic Craniosynostosis.

This study aims to determine the utility of 3D photography for evaluating the severity of metopic craniosynostosis (MCS) using a validated, supervised machine learning (ML) algorithm.This single-center retrospective cohort study included patients who were evaluated at our tertiary care center for MCS from 2016 to 2020 and underwent both head CT and 3D photography within a 2-month period.The analysis method builds on our previously established ML algorithm for evaluating MCS severity using skull shape from CT scans. In this study, we regress the model to analyze 3D photographs and correlate the severity scores from both imaging modalities.14 patients met inclusion criteria, 64.3% male (n = 9). The mean age in years at 3D photography and CT imaging was 0.97 and 0.94, respectively. Ten patient images were obtained preoperatively, and 4 patients did not require surgery. The severity prediction of the ML algorithm correlates closely when comparing the 3D photographs to CT bone data (Spearman correlation coefficient [SCC] r = 0.75; Pearson correlation coefficient [PCC] r = 0.82).The results of this study show that 3D photography is a valid alternative to CT for evaluation of head shape in MCS. Its use will provide an objective, quantifiable means of assessing outcomes in a rigorous manner while decreasing radiation exposure in this patient population.

"Validation of Artificial Intelligence Severity Assessment in Metopic Craniosynostosis".

OBJECTIVE: Several severity metrics have been developed for metopic craniosynostosis, including a recent machine learning-derived algorithm. This study assessed the diagnostic concordance between machine learning and previously published severity indices. DESIGN: Preoperative computed tomography (CT) scans of patients who underwent surgical correction of metopic craniosynostosis were quantitatively analyzed for severity. Each scan was manually measured to derive manual severity scores and also received a scaled metopic severity score (MSS) assigned by the machine learning algorithm. Regression analysis was used to correlate manually captured measurements to MSS. ROC analysis was performed for each severity metric and were compared to how accurately they distinguished cases of metopic synostosis from controls. RESULTS: In total, 194 CT scans were analyzed, 167 with metopic synostosis and 27 controls. The mean scaled MSS for the patients with metopic was 6.18 ± 2.53 compared to 0.60 ± 1.25 for controls. Multivariable regression analyses yielded an R-square of 0.66, with significant manual measurements of endocranial bifrontal angle (EBA) (P = 0.023), posterior angle of the anterior cranial fossa (p < 0.001), temporal depression angle (P = 0.042), age (P < 0.001), biparietal distance (P < 0.001), interdacryon distance (P = 0.033), and orbital width (P < 0.001). ROC analysis demonstrated a high diagnostic value of the MSS (AUC = 0.96, P < 0.001), which was comparable to other validated indices including the adjusted EBA (AUC = 0.98), EBA (AUC = 0.97), and biparietal/bitemporal ratio (AUC = 0.95). CONCLUSIONS: The machine learning algorithm offers an objective assessment of morphologic severity that provides a reliable composite impression of severity. The generated score is comparable to other severity indices in ability to distinguish cases of metopic synostosis from controls.

Relating Metopic Craniosynostosis Severity to Intracranial Pressure.

PURPOSE: A subset of patients with metopic craniosynostosis are noted to have elevated intracranial pressure (ICP). However, it is not known if the propensity for elevated ICP is influenced by the severity of metopic cranial dysmorphology. METHODS: Children with nonsyndromic single-suture metopic synostosis were prospectively enrolled and underwent optical coherence tomography to measure optic nerve head morphology. Preoperative head computed tomography scans were assessed for endocranial bifrontal angle as well as scaled metopic synostosis severity score (MSS) and cranial morphology deviation score determined by CranioRate, an automated severity classifier. RESULTS: Forty-seven subjects were enrolled between 2014 and 2019, at an average age of 8.5 months at preoperative computed tomography and 11.8 months at index procedure. Fourteen patients (29.7%) had elevated optical coherence tomography parameters suggestive of elevated ICP at the time of surgery. Ten patients (21.3%) had been diagnosed with developmental delay, eight of whom demonstrated elevated ICP. There were no significant associations between measures of metopic severity and ICP. Metopic synostosis severity score and endocranial bifrontal angle were inversely correlated, as expected ( r =-0.545, P <0.001). A negative correlation was noted between MSS and formally diagnosed developmental delay ( r =-0.387, P =0.008). Likewise, negative correlations between age at procedure and both MSS and cranial morphology deviation was observed ( r =-0.573, P <0.001 and r =-0.312, P =0.025, respectively). CONCLUSIONS: Increased metopic severity was not associated with elevated ICP at the time of surgery. Patients who underwent later surgical correction showed milder phenotypic dysmorphology with an increased incidence of developmental delay.

Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes.

Short-term exposure to fine particulate matter (PM2.5) pollution is linked to numerous adverse health effects. Pollution episodes, such as wildfires, can lead to substantial increases in PM2.5 levels. However, sparse regulatory measurements provide an incomplete understanding of pollution gradients. Here, we demonstrate an infrastructure that integrates community-based measurements from a network of low-cost PM2.5 sensors with rigorous calibration and a Gaussian process model to understand neighborhood-scale PM2.5 concentrations during three pollution episodes (July 4, 2018, fireworks; July 5 and 6, 2018, wildfire; Jan 3-7, 2019, persistent cold air pool, PCAP). The firework/wildfire events included 118 sensors in 84 locations, while the PCAP event included 218 sensors in 138 locations. The model results accurately predict reference measurements during the fireworks (n: 16, hourly root-mean-square error, RMSE, 12.3-21.5 µg/m3, n(normalized)RMSE: 14.9-24%), the wildfire (n: 46, RMSE: 2.6-4.0 µg/m3; nRMSE: 13.1-22.9%), and the PCAP (n: 96, RMSE: 4.9-5.7 µg/m3; nRMSE: 20.2-21.3%). They also revealed dramatic geospatial differences in PM2.5 concentrations that are not apparent when only considering government measurements or viewing the US Environmental Protection Agency's AirNow visualizations. Complementing the PM2.5 estimates and visualizations are highly resolved uncertainty maps. Together, these results illustrate the potential for low-cost sensor networks that combined with a data-fusion algorithm and appropriate calibration and training can dynamically and with improved accuracy estimate PM2.5 concentrations during pollution episodes. These highly resolved uncertainty estimates can provide a much-needed strategy to communicate uncertainty to end users.

Patterns of distributive environmental inequity under different PM2.5 air pollution scenarios for Salt Lake County public schools.

Previous studies have cataloged social disparities in air pollution exposure in US public schools with respect to race/ethnicity and socioeconomic status. These studies rely upon chronic, averaged measures of air pollution, which fosters a static conception of exposure disparities. This paper examines PM2.5 exposure disparities in Salt Lake County (SLC), Utah public schools under three different PM2.5 scenarios-relatively clean air, a moderate winter persistent cold air pool (PCAP), and a major winter PCAP-with respect to race/ethnicity, economic deprivation, student age, and school type. We pair demographic data for SLC schools (n = 174) with modelled PM2.5 values, obtained from a distributed network of sensors placed through a community-university partnership. Results from generalized estimating equations controlling for school district clustering and other covariates reveal that patterns of social inequality vary under different PM2.5 pollution scenarios. Charter schools and schools serving economically deprived students experienced disproportionate exposure during relatively clean air and moderate PM2.5 PCAP conditions, but those inequalities attenuated under major PCAP conditions. Schools with higher proportions of racial/ethnic minority students were unequally exposed under all PM2.5 pollution scenarios, reflecting the robustness of racial/ethnic disparities in exposure. The findings speak to the need for policy changes to protect school-aged children from environmental harm in SLC and elsewhere.

Quantifying the Severity of Metopic Craniosynostosis: A Pilot Study Application of Machine Learning in Craniofacial Surgery.

The standard for diagnosing metopic craniosynostosis (CS) utilizes computed tomography (CT) imaging and physical exam, but there is no standardized method for determining disease severity. Previous studies using interfrontal angles have evaluated differences in specific skull landmarks; however, these measurements are difficult to readily ascertain in clinical practice and fail to assess the complete skull contour. This pilot project employs machine learning algorithms to combine statistical shape information with expert ratings to generate a novel objective method of measuring the severity of metopic CS.Expert ratings of normal and metopic skull CT images were collected. Skull-shape analysis was conducted using ShapeWorks software. Machine-learning was used to combine the expert ratings with our shape analysis model to predict the severity of metopic CS using CT images. Our model was then compared to the gold standard using interfrontal angles.Seventeen metopic skull CT images of patients 5 to 15 months old were assigned a severity by 18 craniofacial surgeons, and 65 nonaffected controls were included with a 0 severity. Our model accurately correlated the level of skull deformity with severity (P < 0.10) and predicted the severity of metopic CS more often than models using interfrontal angles (χ = 5.46, P = 0.019).This is the first study that combines shape information with expert ratings to generate an objective measure of severity for metopic CS. This method may help clinicians easily quantify the severity and perform robust longitudinal assessments of the condition.

Which Two-dimensional Radiographic Measurements of Cam Femoroacetabular Impingement Best Describe the Three-dimensional Shape of the Proximal Femur?

BACKGROUND: Many two-dimensional (2-D) radiographic views are used to help diagnose cam femoroacetabular impingement (FAI), but there is little consensus as to which view or combination of views is most effective at visualizing the magnitude and extent of the cam lesion (ie, severity). Previous studies have used a single image from a sequence of CT or MR images to serve as a reference standard with which to evaluate the ability of 2-D radiographic views and associated measurements to describe the severity of the cam lesion. However, single images from CT or MRI data may fail to capture the apex of the cam lesion. Thus, it may be more appropriate to use measurements of three-dimensional (3-D) surface reconstructions from CT or MRI data to serve as an anatomic reference standard when evaluating radiographic views and associated measurements used in the diagnosis of cam FAI. QUESTIONS/PURPOSES: The purpose of this study was to use digitally reconstructed radiographs and 3-D statistical shape modeling to (1) determine the correlation between 2-D radiographic measurements of cam FAI and 3-D metrics of proximal femoral shape; and 2) identify the combination of radiographic measurements from plain film projections that were most effective at predicting the 3-D shape of the proximal femur. METHODS: This study leveraged previously acquired CT images of the femur from a convenience sample of 37 patients (34 males; mean age, 27 years, range, 16-47 years; mean body mass index [BMI], 24.6 kg/m, range, 19.0-30.2 kg/m) diagnosed with cam FAI imaged between February 2005 and January 2016. Patients were diagnosed with cam FAI based on a culmination of clinical examinations, history of hip pain, and imaging findings. The control group consisted of 59 morphologically normal control participants (36 males; mean age, 29 years, range, 15-55 years; mean BMI, 24.4 kg/m, range, 16.3-38.6 kg/m) imaged between April 2008 and September 2014. Of these controls, 30 were cadaveric femurs and 29 were living participants. All controls were screened for evidence of femoral deformities using radiographs. In addition, living control participants had no history of hip pain or previous surgery to the hip or lower limbs. CT images were acquired for each participant and the surface of the proximal femur was segmented and reconstructed. Surfaces were input to our statistical shape modeling pipeline, which objectively calculated 3-D shape scores that described the overall shape of the entire proximal femur and of the region of the femur where the cam lesion is typically located. Digital reconstructions for eight plain film views (AP, Meyer lateral, 45° Dunn, modified 45° Dunn, frog-leg lateral, Espié frog-leg, 90° Dunn, and cross-table lateral) were generated from CT data. For each view, measurements of the α angle and head-neck offset were obtained by two researchers (intraobserver correlation coefficients of 0.80-0.94 for the α angle and 0.42-0.80 for the head-neck offset measurements). The relationships between radiographic measurements from each view and the 3-D shape scores (for the entire proximal femur and for the region specific to the cam lesion) were assessed with linear correlation. Additionally, partial least squares regression was used to determine which combination of views and measurements was the most effective at predicting 3-D shape scores. RESULTS: Three-dimensional shape scores were most strongly correlated with α angle on the cross-table view when considering the entire proximal femur (r = -0.568; p < 0.001) and on the Meyer lateral view when considering the region of the cam lesion (r = -0.669; p < 0.001). Partial least squares regression demonstrated that measurements from the Meyer lateral and 90° Dunn radiographs produced the optimized regression model for predicting shape scores for the proximal femur (R = 0.405, root mean squared error of prediction [RMSEP] = 1.549) and the region of the cam lesion (R = 0.525, RMSEP = 1.150). Interestingly, views with larger differences in the α angle and head-neck offset between control and cam FAI groups did not have the strongest correlations with 3-D shape. CONCLUSIONS: Considered together, radiographic measurements from the Meyer lateral and 90° Dunn views provided the most effective predictions of 3-D shape of the proximal femur and the region of the cam lesion as determined using shape modeling metrics. CLINICAL RELEVANCE: Our results suggest that clinicians should consider using the Meyer lateral and 90° Dunn views to evaluate patients in whom cam FAI is suspected. However, the α angle and head-neck offset measurements from these and other plain film views could describe no more than half of the overall variation in the shape of the proximal femur and cam lesion. Thus, caution should be exercised when evaluating femoral head anatomy using the α angle and head-neck offset measurements from plain film radiographs. Given these findings, we believe there is merit in pursuing research that aims to develop the framework necessary to integrate statistical shape modeling into clinical evaluation, because this could aid in the diagnosis of cam FAI.

Does Removal of Subchondral Cortical Bone Provide Sufficient Resection Depth for Treatment of Cam Femoroacetabular Impingement?

BACKGROUND: Residual impingement resulting from insufficient resection of bone during the index femoroplasty is the most-common reason for revision surgery in patients with cam-type femoroacetabular impingement (FAI). Development of surgical resection guidelines therefore could reduce the number of patients with persistent pain and reduced ROM after femoroplasty. QUESTIONS/PURPOSES: We asked whether removal of subchondral cortical bone in the region of the lesion in patients with cam FAI could restore femoral anatomy to that of screened control subjects. To evaluate this, we analyzed shape models between: (1) native cam and screened control femurs to observe the location of the cam lesion and establish baseline shape differences between groups, and (2) cam femurs with simulated resections and screened control femurs to evaluate the sufficiency of subchondral cortical bone thickness to guide resection depth. METHODS: Three-dimensional (3-D) reconstructions of the inner and outer cortical bone boundaries of the proximal femur were generated by segmenting CT images from 45 control subjects (29 males; 15 living subjects, 30 cadavers) with normal radiographic findings and 28 nonconsecutive patients (26 males) with a diagnosis of cam FAI based on radiographic measurements and clinical examinations. Correspondence particles were placed on each femur and statistical shape modeling (SSM) was used to create mean shapes for each cohort. The geometric difference between the mean shape of the patients with cam FAI and that of the screened controls was used to define a consistent region representing the cam lesion. Subchondral cortical bone in this region was removed from the 3-D reconstructions of each cam femur to create a simulated resection. SSM was repeated to determine if the resection produced femoral anatomy that better resembled that of control subjects. Correspondence particle locations were used to generate mean femur shapes and evaluate shape differences using principal component analysis. RESULTS: In the region of the cam lesion, the median distance between the mean native cam and control femurs was 1.8 mm (range, 1.0-2.7 mm). This difference was reduced to 0.2 mm (range, -0.2 to 0.9 mm) after resection, with some areas of overresection anteriorly and underresection superiorly. In the region of resection for each subject, the distance from each correspondence particle to the mean control shape was greater for the cam femurs than the screened control femurs (1.8 mm, [range, 1.1-2.9 mm] and 0.0 mm [range, -0.2-0.1 mm], respectively; p < 0.031). After resection, the distance was not different between the resected cam and control femurs (0.3 mm; range, -0.2-1.0; p > 0.473). CONCLUSIONS: Removal of subchondral cortical bone in the region of resection reduced the deviation between the mean resected cam and control femurs to within a millimeter, which resulted in no difference in shape between patients with cam FAI and control subjects. Collectively, our results support the use of the subchondral cortical-cancellous bone margin as a visual intraoperative guide to limit resection depth in the correction of cam FAI. CLINICAL RELEVANCE: Use of the subchondral cortical-cancellous bone boundary may provide a method to guide the depth of resection during arthroscopic surgery, which can be observed intraoperatively without advanced tooling, or imaging.

Architecting the Finite Element Method Pipeline for the GPU.

The finite element method (FEM) is a widely employed numerical technique for approximating the solution of partial differential equations (PDEs) in various science and engineering applications. Many of these applications benefit from fast execution of the FEM pipeline. One way to accelerate the FEM pipeline is by exploiting advances in modern computational hardware, such as the many-core streaming processors like the graphical processing unit (GPU). In this paper, we present the algorithms and data-structures necessary to move the entire FEM pipeline to the GPU. First we propose an efficient GPU-based algorithm to generate local element information and to assemble the global linear system associated with the FEM discretization of an elliptic PDE. To solve the corresponding linear system efficiently on the GPU, we implement a conjugate gradient method preconditioned with a geometry-informed algebraic multi-grid (AMG) method preconditioner. We propose a new fine-grained parallelism strategy, a corresponding multigrid cycling stage and efficient data mapping to the many-core architecture of GPU. Comparison of our on-GPU assembly versus a traditional serial implementation on the CPU achieves up to an 87 × speedup. Focusing on the linear system solver alone, we achieve a speedup of up to 51 × versus use of a comparable state-of-the-art serial CPU linear system solver. Furthermore, the method compares favorably with other GPU-based, sparse, linear solvers.

DeepSSM: A blueprint for image-to-shape deep learning models.

Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. Statistical analysis of shapes requires consistent shape representation across samples in shape cohort. Establishing this representation entails a processing pipeline that includes anatomy segmentation, image re-sampling, shape-based registration, and non-linear, iterative optimization. These shape representations are then used to extract low-dimensional shape descriptors that are anatomically relevant to facilitate subsequent statistical analyses in different applications. However, the current process of obtaining these shape descriptors from imaging data relies on human and computational resources, requiring domain expertise for segmenting anatomies of interest. Moreover, this same taxing pipeline needs to be repeated to infer shape descriptors for new image data using a pre-trained/existing shape model. Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images. Once trained using an existing shape model, DeepSSM circumvents the heavy and manual pre-processing and segmentation required by classical models and significantly improves the computational time, making it a viable solution for fully end-to-end shape modeling applications. In addition, we introduce a model-based data-augmentation strategy to address data scarcity, a typical scenario in shape modeling applications. Finally, this paper presents and analyzes two different architectural variants of DeepSSM with different loss functions using three medical datasets and their downstream clinical application. Experiments showcase that DeepSSM performs comparably or better to the state-of-the-art SSM both quantitatively and on application-driven downstream tasks. Therefore, DeepSSM aims to provide a comprehensive blueprint for deep learning-based image-to-shape models.

CranioRate: An Image-Based, Deep-Phenotyping Analysis Toolset and Online Clinician Interface for Metopic Craniosynostosis.

BACKGROUND: The diagnosis and management of metopic craniosynostosis involve subjective decision-making at the point of care. The purpose of this work was to describe a quantitative severity metric and point-of-care user interface to aid clinicians in the management of metopic craniosynostosis and to provide a platform for future research through deep phenotyping. METHODS: Two machine-learning algorithms were developed that quantify the severity of craniosynostosis-a supervised model specific to metopic craniosynostosis [Metopic Severity Score (MSS)] and an unsupervised model used for cranial morphology in general [Cranial Morphology Deviation (CMD)]. Computed tomographic (CT) images from multiple institutions were compiled to establish the spectrum of severity, and a point-of-care tool was developed and validated. RESULTS: Over the study period (2019 to 2021), 254 patients with metopic craniosynostosis and 92 control patients who underwent CT scanning between the ages of 6 and 18 months were included. CT scans were processed using an unsupervised machine-learning based dysmorphology quantification tool, CranioRate. The average MSS was 0.0 ± 1.0 for normal controls and 4.9 ± 2.3 ( P < 0.001) for those with metopic synostosis. The average CMD was 85.2 ± 19.2 for normal controls and 189.9 ± 43.4 ( P < 0.001) for those with metopic synostosis. A point-of-care user interface (craniorate.org) has processed 46 CT images from 10 institutions. CONCLUSIONS: The resulting quantification of severity using MSS and CMD has shown an improved capacity, relative to conventional measures, to automatically classify normal controls versus patients with metopic synostosis. The authors have mathematically described, in an objective and quantifiable manner, the distribution of phenotypes in metopic craniosynostosis.

Multi-Task Classification for Improved Health Outcome Prediction Based on Environmental Indicators.

This paper aims to address the challenges associated with evaluating the impact of neighborhood environments on health outcomes. Google street view (GSV) images provide a valuable tool for assessing neighborhood environments on a large scale. By annotating the GSV images with labels indicating the presence or absence of specific neighborhood features, we can develop classifiers capable of automatically analyzing and evaluating the environment. However, the process of labeling GSV images to analyze and evaluate the environment is a time-consuming and labor-intensive task. To overcome these challenges, we propose using a multi-task classifier to enhance the training of classifiers with limited supervised GSV data. Our multi-task classifier utilizes readily available, inexpensive online images collected from Flickr as a related classification task. The hypothesis is that a classifier trained on multiple related tasks is less likely to overfit to small amounts of training data and generalizes better to unseen data. We leverage the power of multiple related tasks to improve the classifier's overall performance and generalization capability. Here we show that, with the proposed learning paradigm, predicted labels for GSV test images are more accurate. Across different environment indicators, the accuracy, F1 score and balanced accuracy increase up to 6 % in the multi-task learning framework compared to its single-task learning counterpart. The enhanced accuracy of the predicted labels obtained through the multi-task classifier contributes to a more reliable and precise regression analysis determining the correlation between predicted built environment indicators and health outcomes. The R2 values calculated for different health outcomes improve by up to 4 % using multi-task learning detected indicators.

Quantifying the Severity of Metopic Craniosynostosis Using Unsupervised Machine Learning.

BACKGROUND: Quantifying the severity of head shape deformity and establishing a threshold for operative intervention remains challenging in patients with metopic craniosynostosis (MCS). This study combines three-dimensional skull shape analysis with an unsupervised machine-learning algorithm to generate a quantitative shape severity score (cranial morphology deviation) and provide an operative threshold score. METHODS: Head computed tomography scans from subjects with MCS and normal controls (5 to 15 months of age) were used for objective three-dimensional shape analysis using ShapeWorks software and in a survey for craniofacial surgeons to rate head-shape deformity and report whether they would offer surgical correction based on head shape alone. An unsupervised machine-learning algorithm was developed to quantify the degree of shape abnormality of MCS skulls compared to controls. RESULTS: One hundred twenty-four computed tomography scans were used to develop the model; 50 (24% MCS, 76% controls) were rated by 36 craniofacial surgeons, with an average of 20.8 ratings per skull. The interrater reliability was high (intraclass correlation coefficient, 0.988). The algorithm performed accurately and correlates closely with the surgeons assigned severity ratings (Spearman correlation coefficient, r = 0.817). The median cranial morphology deviation for affected skulls was 155.0 (interquartile range, 136.4 to 194.6; maximum, 231.3). Skulls with ratings of 150.2 or higher were very likely to be offered surgery by the experts in this study. CONCLUSIONS: This study describes a novel metric to quantify the head shape deformity associated with MCS and contextualizes the results using clinical assessments of head shapes by craniofacial experts. This metric may be useful in supporting clinical decision making around operative intervention and in describing outcomes and comparing patient population across centers.

A computational framework for ultrastructural mapping of neural circuitry.

Circuitry mapping of metazoan neural systems is difficult because canonical neural regions (regions containing one or more copies of all components) are large, regional borders are uncertain, neuronal diversity is high, and potential network topologies so numerous that only anatomical ground truth can resolve them. Complete mapping of a specific network requires synaptic resolution, canonical region coverage, and robust neuronal classification. Though transmission electron microscopy (TEM) remains the optimal tool for network mapping, the process of building large serial section TEM (ssTEM) image volumes is rendered difficult by the need to precisely mosaic distorted image tiles and register distorted mosaics. Moreover, most molecular neuronal class markers are poorly compatible with optimal TEM imaging. Our objective was to build a complete framework for ultrastructural circuitry mapping. This framework combines strong TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration, and gigabyte-scale image browsing for volume annotation. Specifically we show how ultrathin molecular profiling datasets and their resultant classification maps can be embedded into ssTEM datasets and how scripted acquisition tools (SerialEM), mosaicking and registration (ir-tools), and large slice viewers (MosaicBuilder, Viking) can be used to manage terabyte-scale volumes. These methods enable large-scale connectivity analyses of new and legacy data. In well-posed tasks (e.g., complete network mapping in retina), terabyte-scale image volumes that previously would require decades of assembly can now be completed in months. Perhaps more importantly, the fusion of molecular profiling, image acquisition by SerialEM, ir-tools volume assembly, and data viewers/annotators also allow ssTEM to be used as a prospective tool for discovery in nonneural systems and a practical screening methodology for neurogenetics. Finally, this framework provides a mechanism for parallelization of ssTEM imaging, volume assembly, and data analysis across an international user base, enhancing the productivity of a large cohort of electron microscopists.

A Nonparametric Approach for Estimating Three-Dimensional Fiber Orientation Distribution Functions (ODFs) in Fibrous Materials.

Many biological tissues contain an underlying fibrous microstructure that is optimized to suit a physiological function. The fiber architecture dictates physical characteristics such as stiffness, diffusivity, and electrical conduction. Abnormal deviations of fiber architecture are often associated with disease. Thus, it is useful to characterize fiber network organization from image data in order to better understand pathological mechanisms. We devised a method to quantify distributions of fiber orientations based on the Fourier transform and the Qball algorithm from diffusion MRI. The Fourier transform was used to decompose images into directional components, while the Qball algorithm efficiently converted the directional data from the frequency domain to the orientation domain. The representation in the orientation domain does not require any particular functional representation, and thus the method is nonparametric. The algorithm was verified to demonstrate its reliability and used on datasets from microscopy to show its applicability. This method increases the ability to extract information of microstructural fiber organization from experimental data that will enhance our understanding of structure-function relationships and enable accurate representation of material anisotropy in biological tissues.

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes.

Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health.