skip to main content

This content will become publicly available on April 1, 2023

Title: Measuring deformational plagiocephaly and brachycephaly using a smartphone in a prospective study
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, more » 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
; ; ; ; ;
Award ID(s):
Publication Date:
Journal Name:
American Cleft Palate-Craniofacial Association 79th Annual Meeting, 2022
Sponsoring Org:
National Science Foundation
More Like this
  1. 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 prospectivelymore »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.« 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. Biomarkers including reproductive hormones and indicators of energy balance can be used to analyze health status and physiology in wild animals. Non-invasive collection of urine or feces enables biomarker monitoring, important for critically endangered species like orangutans. Hormonal measurements must control for urine concentration, typically done using creatinine or specific gravity. Specific gravity measurement compares the density of urine with the density of water. Creatinine is a breakdown product of muscle metabolism that is excreted from the body at a relatively stable rate, and it is an indicator of relative muscle mass in many species. Here, we measure specific gravitymore »in urine samples from captive female orangutans using a digital hand-held urine specific gravity refractometer. We compare specific gravity to previously measured creatinine values and assess the influence of time of collection and refractometer temperature on specific gravity. We found a significant positive correlation between specific gravity and creatinine concentrations (N=1021, Pearson’s R=0.578, p<0.001). While we found no significant correlation between the time that samples were collected and specific gravity readings (N= 314, Pearson’s R=0.079, p=0.17), readings from morning samples were slightly but significantly lower (N=255, mean=1.008) than afternoon samples (N=60, mean=1.009) (independent samples t-test, t312=-1.969, p=0.05). We found a significant negative correlation between specific gravity and refractometer temperature (Pearson’s R=-0.23, p<0.001), highlighting the need to control for urine temperature when using thawed samples.« less
  4. The standard of clinical care in many pediatric and neonatal neurocritical care units involves continuous monitoring of cerebral hemodynamics using hard-wired devices that physically adhere to the skin and connect to base stations that commonly mount on an adjacent wall or stand. Risks of iatrogenic skin injuries associated with adhesives that bond such systems to the skin and entanglements of the patients and/or the healthcare professionals with the wires can impede clinical procedures and natural movements that are critical to the care, development, and recovery of pediatric patients. This paper presents a wireless, miniaturized, and mechanically soft, flexible device thatmore »supports measurements quantitatively comparable to existing clinical standards. The system features a multiphotodiode array and pair of light-emitting diodes for simultaneous monitoring of systemic and cerebral hemodynamics, with ability to measure cerebral oxygenation, heart rate, peripheral oxygenation, and potentially cerebral pulse pressure and vascular tone, through the utilization of multiwavelength reflectance-mode photoplethysmography and functional near-infrared spectroscopy. Monte Carlo optical simulations define the tissue-probing depths for source–detector distances and operating wavelengths of these systems using magnetic resonance images of the head of a representative pediatric patient to define the relevant geometries. Clinical studies on pediatric subjects with and without congenital central hypoventilation syndrome validate the feasibility for using this system in operating hospitals and define its advantages relative to established technologies. This platform has the potential to substantially enhance the quality of pediatric care across a wide range of conditions and use scenarios, not only in advanced hospital settings but also in clinics of lower- and middle-income countries.« less
  5. Objective: The objective of the study is to build models for early prediction of risk for developing multiple organ dysfunction (MOD) in pediatric intensive care unit (PICU) patients. Design: The design of the study is a retrospective observational cohort study. Setting: The setting of the study is at a single academic PICU at the Johns Hopkins Hospital, Baltimore, MD. Patients: The patients included in the study were <18 years of age admitted to the PICU between July 2014 and October 2015. Measurements and main results: Organ dysfunction labels were generated every minute from preceding 24-h time windows using the Internationalmore »Pediatric Sepsis Consensus Conference (IPSCC) and Proulx et al. MOD criteria. Early MOD prediction models were built using four machine learning methods: random forest, XGBoost, GLMBoost, and Lasso-GLM. An optimal threshold learned from training data was used to detect high-risk alert events (HRAs). The early prediction models from all methods achieved an area under the receiver operating characteristics curve ≥0.91 for both IPSCC and Proulx criteria. The best performance in terms of maximum F1-score was achieved with random forest (sensitivity: 0.72, positive predictive value: 0.70, F1-score: 0.71) and XGBoost (sensitivity: 0.8, positive predictive value: 0.81, F1-score: 0.81) for IPSCC and Proulx criteria, respectively. The median early warning time was 22.7 h for random forest and 37 h for XGBoost models for IPSCC and Proulx criteria, respectively. Applying spectral clustering on risk-score trajectories over 24 h following early warning provided a high-risk group with ≥0.93 positive predictive value. Conclusions: Early predictions from risk-based patient monitoring could provide more than 22 h of lead time for MOD onset, with ≥0.93 positive predictive value for a high-risk group identified pre-MOD.« less