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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). 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.
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Digital Head Circumference Measurement at the Point-of-Care
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.
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- Award ID(s):
- 2036061
- PAR ID:
- 10295846
- Date Published:
- Journal Name:
- Digital Head Circumference Measurement at the Point-of-Care
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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 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.more » « less
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Abstract Both high‐power large aperture radars and smaller meteor radars readily observe the dense head plasma produced as a meteoroid ablates. However, determining the mass of such meteors based on the information returned by the radar is challenging. We present a new method for deriving meteor masses from single‐frequency radar measurements, using a physics‐based plasma model and finite‐difference time‐domain (FDTD) simulations. The head plasma model derived in Dimant and Oppenheim (2017),https://doi.org/10.1002/2017ja023963depends on the meteoroids altitude, speed, and size. We use FDTD simulations of a radar pulse interacting with such head plasmas to determine the radar cross section (RCS) that a radar system would observe for a meteor with a given set of physical properties. By performing simulations over the observed parameter space, we construct tables relating meteor size, velocity, and altitude to RCS. We then use these tables to map a set of observations from the MAARSY radar (53.5 MHz) to fully defined plasma distributions, from which masses are calculated. To validate these results, we repeat the analysis using observations of the same meteors by the EISCAT radar (929 MHz). The resulting masses are strongly linearly correlated; however, the masses derived from EISCAT measurements are on average 1.33 times larger than those derived from MAARSY measurements. Since this method does not require dual‐frequency measurements for mass determination, only validation, it can be applied in the future to observations made by many single‐frequency radar systems.more » « less
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