skip to main content

This content will become publicly available on May 1, 2023

Title: Smartphone-based Measurements of Deformational Plagiocephaly and Brachycephaly: A Prospective Study
Background: Deformational plagiocephaly and brachycephaly (DPB) is manifested in ~20% of newborns in the US. DPB can be effectively corrected by repositioning and/or physical therapy if detected and monitored before 4 months of age. The cranial index (CI) and cranial vault asymmetry index (CVAI) are used for DPB diagnosis and monitoring. As there is no current tool available for pediatricians or parents to quantitatively measure these indices at the point-of-care, we developed a smartphone app, called SoftSpot, that measures CI and CVAI from photographs of a child’s head to increase the chances of early detection and treatment. Objective: To prospectively evaluate the accuracy of the smartphone measurements of CI and CVAI in a clinical setting. Methods: Bird’s eye-view head photos of 117 infants aged 2-11 months (42 female, 75 male) were captured at a large multidisciplinary craniofacial center with the SoftSpot app (PediaMetrix Inc. Rockville, MD) using an iPhone X (Apple Inc., Cupertino, CA). The study was IRB approved and parent consent was obtained. Measurements included width, length, and diagonals of the patients’ head were obtained by a single CRNP and were used to calculate CI and CVAI as the ground truth. At least five images for each patient were chosen more » by an analyst, CI and CVAI were automatically measured by the proprietary algorithms of the app, and results were averaged for each patient. Automated and ground truth CI and CVAI measurements were compared using the Bland-Altman method and Spearman Correlation Coefficient after excluding outliers with mean absolute error (MAE) greater than two standard deviations. Results: MAE was 2.47 ± 1.68 for CI, 1.55 ± 1.03 for CVAI. Spearman correlation coefficients were 0.93 and 0.91 (p-values < 0.001) for CI and CVAI, respectively (see Fig. 2). Bland-Altman analysis (see Fig. 2 resulted in limits of agreement of [-4.41, 6.53] for CI and [-3.64, 3.68] for CVAI, with respective biases of 1.06 and 0.02. Conclusion: Our app measures CI and CVAI from head 2D photos with very high correlation to caliper-based measurements obtained in the craniofacial clinic. This prospective study demonstrates the clinical feasibility of using a smartphone app for cranial measurements at the point-of-care with the potential to early detect and monitor DPB. The app can potentially be used in telemedicine encounters when in-person visits are difficult due to circumstances like COVID-19 or for remote and underserved areas. « less
; ; ; ; ;
Award ID(s):
Publication Date:
Journal Name:
Pediatric Academic Societies Meeting 2022
Sponsoring Org:
National Science Foundation
More Like this
  1. Background & Purpose: Deformational plagiocephaly and brachycephaly (DPB) is a cranial condition manifested in 20% of infants in the US. DPB affects children and their families through psychological pressure, social stigma, and significant medical costs. If detected between 0-3 months of age, there is strong potential for correction via aggressive repositioning and/or physical therapy if congenital muscular torticollis is present. At later stages, DPB is most effectively treated by more expensive treatments like helmet therapy. Two cranial parameters that can help with the early detection and tracking of DPB are the cranial index (CI) and cranial vault asymmetry index (CVAI).more »Currently, these measurements are performed with a hand caliper by a specialist, i.e., nurse practitioner (CRNP) or physician assistant who specializes in cleft-craniofacial diagnosis, physical therapist, pediatric plastic/neurosurgeons, or orthotist. To make the measurements frequent, accessible, and accurate at the point of care, i.e., in pediatric offices, we developed and evaluated a mobile app called SoftspotTM to measure CI and CVAI, thus facilitating the early detection and monitoring of DPB. Method/Description: Sequences of bird’s eye-view head photos extracted from video were collected for 77 patients (aged 2 – 11 months, 51 females, 26 males) with an iPhone X (Apple Inc., Cupertino, CA). The head length, width, and diagonals were measured by a single CRNP via hand calipers at a large multidisciplinary cranio-facial center with IRB approval and patient consent. For each patient, five images were chosen by an analyst and segmented into head components, namely the head and nose, using quantitative imaging methods. For each image CI and CVAI were automatically measured, and these measurements were averaged for each patient. Automated CI and CVAI measurements were compared to values obtained by the caliper measurements in terms of mean absolute error (MAE), and outliers were excluded beyond 3 standard deviations away from the average MAE. Results were further analyzed by the Bland-Altman method and Spearman Correlation Coefficient. Results: MAE was 2.18 ± 1.60 for CI and 1.57 ± 1.03 for CVAI measurements. Spearman Correlation Coefficients between measurements and ground truth were 0.93 for CI (p<0.001) and 0.91 for CVAI (p<0.001). Bland-Altman analysis revealed limits of agreement for CI and CVAI as [-4.59, 5.76] (mean = 0.59) and [-3.91, 3.40] (mean = -0.25) respectively. Conclusions: Digital smartphone-based methods for DPB assessment are feasible, and this study demonstrated significant correlation between automated digital measurements and ground truth clinical values. Smartphone-based measurements of DPB can be performed at the point of care to improve the early detection and treatment of DPB.« less
  2. Our digital method can measure head shape parameters from head photos with comparable accuracy to expert caliper measurements. This method can be deployed via a smartphone app to enable frequent infant cranial measurements at the point-of-care, and provide decision support tool for pediatricians and care givers.
  3. Introduction: Back pain is one of the most common causes of pain in the United States. Spinal cord stimulation (SCS) is an intervention for patients with chronic back pain (CBP). However, SCS decreases pain in only 58% of patients and relies on self-reported pain scores as outcome measures. An SCS trial is temporarily implanted for seven days and helps to determine if a permanent SCS is needed. Patients that have a >50% reduction in pain from the trial stimulator makes them eligible for permanent implantation. However, self-reported measures reveal little on how mechanisms in the brain are altered. Other measurementsmore »of pain intensity, onset, medication, disabilities, depression, and anxiety have been used with machine learning to predict outcomes with accuracies <70%. We aim to predict long-term SCS responders at 6-months using baseline resting EEG and machine learning. Materials and Methods: We obtained 10-minutes of resting electroencephalography (EEG) and pain questionnaires from nine participants with CBP at two time points: 1) pre-trial baseline. 2) Six months after SCS permanent implant surgery. Subjects were designated as high or moderate responders based on the amount of pain relief provided by the long-term (post six months) SCS, and pain scored on a scale of 0-10 with 0 being no pain and 10 intolerable. We used the resting EEG from baseline to predict long-term treatment outcome. Resting EEG data was fed through a pipeline for classification and to map dipole sources. EEG signals were preprocessed using the EEGLAB toolbox. Independent component analysis and dipole fitting were used to linearly unmix the signal and to map dipole sources from the brain. Spectral analysis was performed to obtain the frequency distribution of the signal. Each power band, delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-100 Hz), as well as the entire spectrum (1-100 Hz), were used for classification. Furthermore, dipole sources were ranked based on classification feature weights to determine the significance of specific regions in the brain. We used support vector machines to predict pain outcomes. Results and Discussion: We found higher frequency powerbands provide overall classification performance of 88.89%. Differences in power are seen between moderate and high responders in both the frontal and parietal regions for theta, alpha, beta, and the entire spectrum (Fig.1). This can potentially be used to predict patient response to SCS. Conclusions: We found evidence of decreased power in theta, alpha, beta, and entire spectrum in the anterior regions of the parietal cortex and posterior regions of the frontal cortex between moderate and high responders, which can be used for predicting treatment outcomes in long-term pain relief from SCS. Long-term treatment outcome prediction using baseline EEG data has the potential to contribute to decision making in terms of permanent surgery, forgo trial periods, and improve clinical efficiency by beginning to understand the mechanism of action of SCS in the human brain.« less
  4. Abstract STUDY QUESTION To what extent is exposure to cellular telephones associated with male fertility? SUMMARY ANSWER Overall, we found little association between carrying a cell phone in the front pants pocket and male fertility, although among leaner men (BMI <25 kg/m2), carrying a cell phone in the front pants pocket was associated with lower fecundability. WHAT IS KNOWN ALREADY Some studies have indicated that cell phone use is associated with poor semen quality, but the results are conflicting. STUDY DESIGN, SIZE, DURATION Two prospective preconception cohort studies were conducted with men in Denmark (n = 751) and in North America (n = 2349), enrolledmore »and followed via the internet from 2012 to 2020. PARTICIPANTS/MATERIALS, SETTING, METHODS On the baseline questionnaire, males reported their hours/day of carrying a cell phone in different body locations. We ascertained time to pregnancy via bi-monthly follow-up questionnaires completed by the female partner for up to 12 months or until reported conception. We used proportional probabilities regression models to estimate fecundability ratios (FRs) and 95% confidence intervals (CIs) for the association between male cell phone habits and fecundability, focusing on front pants pocket exposure, within each cohort separately and pooling across the cohorts using a fixed-effect meta-analysis. In a subset of participants, we examined selected semen parameters (semen volume, sperm concentration and sperm motility) using a home-based semen testing kit. MAIN RESULTS AND THE ROLE OF CHANCE There was little overall association between carrying a cell phone in a front pants pocket and fecundability: the FR for any front pants pocket exposure versus none was 0.94 (95% CI: 0.0.83–1.05). We observed an inverse association between any front pants pocket exposure and fecundability among men whose BMI was <25 kg/m2 (FR = 0.72, 95% CI: 0.59–0.88) but little association among men whose BMI was ≥25 kg/m2 (FR = 1.05, 95% CI: 0.90–1.22). There were few consistent associations between cell phone exposure and semen volume, sperm concentration, or sperm motility. LIMITATIONS, REASONS FOR CAUTION Exposure to radiofrequency radiation from cell phones is subject to considerable non-differential misclassification, which would tend to attenuate the estimates for dichotomous comparisons and extreme exposure categories (e.g. exposure 8 vs. 0 h/day). Residual confounding by occupation or other unknown or poorly measured factors may also have affected the results. WIDER IMPLICATIONS OF THE FINDINGS Overall, there was little association between carrying one’s phone in the front pants pocket and fecundability. There was a moderate inverse association between front pants pocket cell phone exposure and fecundability among men with BMI <25 kg/m2, but not among men with BMI ≥25 kg/m2. Although several previous studies have indicated associations between cell phone exposure and lower sperm motility, we found few consistent associations with any semen quality parameters. STUDY FUNDING/COMPETING INTEREST(S) The study was funded by the National Institutes of Health, grant number R03HD090315. In the last 3 years, PRESTO has received in-kind donations from Sandstone Diagnostics (for semen kits), Swiss Precision Diagnostics (home pregnancy tests), (fertility app), and (fertility app). Dr. L.A.W. is a fibroid consultant for AbbVie, Inc. Dr. H.T.S. reports that the Department of Clinical Epidemiology is involved in studies with funding from various companies as research grants to and administered by Aarhus University. None of these studies are related to the current study. Dr. M.L.E. is an advisor to Sandstone Diagnostics, Ro, Dadi, Hannah, and Underdog. Dr. G.J.S. holds ownership in Sandstone Diagnostics Inc., developers of the Trak Male Fertility Testing System. In addition, Dr. G.J.S. has a patent pending related to Trak Male Fertility Testing System issued. TRIAL REGISTRATION NUMBER N/A« less
  5. Introduction: Vaso-occlusive crises (VOCs) are a leading cause of morbidity and early mortality in individuals with sickle cell disease (SCD). These crises are triggered by sickle red blood cell (sRBC) aggregation in blood vessels and are influenced by factors such as enhanced sRBC and white blood cell (WBC) adhesion to inflamed endothelium. Advances in microfluidic biomarker assays (i.e., SCD Biochip systems) have led to clinical studies of blood cell adhesion onto endothelial proteins, including, fibronectin, laminin, P-selectin, ICAM-1, functionalized in microchannels. These microfluidic assays allow mimicking the physiological aspects of human microvasculature and help characterize biomechanical properties of adhered sRBCsmore »under flow. However, analysis of the microfluidic biomarker assay data has so far relied on manual cell counting and exhaustive visual morphological characterization of cells by trained personnel. Integrating deep learning algorithms with microscopic imaging of adhesion protein functionalized microfluidic channels can accelerate and standardize accurate classification of blood cells in microfluidic biomarker assays. Here we present a deep learning approach into a general-purpose analytical tool covering a wide range of conditions: channels functionalized with different proteins (laminin or P-selectin), with varying degrees of adhesion by both sRBCs and WBCs, and in both normoxic and hypoxic environments. Methods: Our neural networks were trained on a repository of manually labeled SCD Biochip microfluidic biomarker assay whole channel images. Each channel contained adhered cells pertaining to clinical whole blood under constant shear stress of 0.1 Pa, mimicking physiological levels in post-capillary venules. The machine learning (ML) framework consists of two phases: Phase I segments pixels belonging to blood cells adhered to the microfluidic channel surface, while Phase II associates pixel clusters with specific cell types (sRBCs or WBCs). Phase I is implemented through an ensemble of seven generative fully convolutional neural networks, and Phase II is an ensemble of five neural networks based on a Resnet50 backbone. Each pixel cluster is given a probability of belonging to one of three classes: adhered sRBC, adhered WBC, or non-adhered / other. Results and Discussion: We applied our trained ML framework to 107 novel whole channel images not used during training and compared the results against counts from human experts. As seen in Fig. 1A, there was excellent agreement in counts across all protein and cell types investigated: sRBCs adhered to laminin, sRBCs adhered to P-selectin, and WBCs adhered to P-selectin. Not only was the approach able to handle surfaces functionalized with different proteins, but it also performed well for high cell density images (up to 5000 cells per image) in both normoxic and hypoxic conditions (Fig. 1B). The average uncertainty for the ML counts, obtained from accuracy metrics on the test dataset, was 3%. This uncertainty is a significant improvement on the 20% average uncertainty of the human counts, estimated from the variance in repeated manual analyses of the images. Moreover, manual classification of each image may take up to 2 hours, versus about 6 minutes per image for the ML analysis. Thus, ML provides greater consistency in the classification at a fraction of the processing time. To assess which features the network used to distinguish adhered cells, we generated class activation maps (Fig. 1C-E). These heat maps indicate the regions of focus for the algorithm in making each classification decision. Intriguingly, the highlighted features were similar to those used by human experts: the dimple in partially sickled RBCs, the sharp endpoints for highly sickled RBCs, and the uniform curvature of the WBCs. Overall the robust performance of the ML approach in our study sets the stage for generalizing it to other endothelial proteins and experimental conditions, a first step toward a universal microfluidic ML framework targeting blood disorders. Such a framework would not only be able to integrate advanced biophysical characterization into fast, point-of-care diagnostic devices, but also provide a standardized and reliable way of monitoring patients undergoing targeted therapies and curative interventions, including, stem cell and gene-based therapies for SCD. Disclosures Gurkan: Dx Now Inc.: Patents & Royalties; Xatek Inc.: Patents & Royalties; BioChip Labs: Patents & Royalties; Hemex Health, Inc.: Consultancy, Current Employment, Patents & Royalties, Research Funding.« less