Proceedings Article | 15 February 2021
KEYWORDS: Head, Point-of-care devices, Cameras, Algorithm development, Tablets, Photography, 3D scanning, 3D metrology
Purpose: to develop and validate algorithms that enable a novice user to quantitatively measure the head shape parameters associated with deformational plagiocephaly, and brachycephaly (DPB) using a smartphone or tablet. Method: We have developed a technology (called SoftSpotTM) based on advanced imaging algorithms to detect different types and severity of DPB from top-view photos of the head acquired by a novice user at the point-of-care, i.e. in pediatric offices, and at home. Currently, the head shape parameters are measured using either a 3D scanner or a mechanical caliper by a specialist such a pediatric neurosurgeon or an orthotist. In our approach, the head contour is extracted semiautomatically using the intelligent scissors method. We then automatically compute two indices used in the clinical determination of the DPB: the cranial index (CI), and the cranial vault asymmetry index (CVAI). In this paper, we also present methods to quantify, and compensate for the user variability in the acquisition of photos, including camera angle, and distance from the head, by combining the results from different camera positions. We compared the results of our technology with ground truth measurements from 53 infants with DPB, and normal cranial parameters. Accuracy analysis was performed by Bland Altman (BA) method, and the Spearman correlation test. Results: The Spearman correlation coefficients between the new 2D method, and the 3D ground truth were 0.94 (p<0.001), and 0.96 (p<0.001) for CI and CVAI, respectively. Different camera angles, and distances from the head resulted in variation in CI and CVAI in the range of [-2.0, 6.0], and [-4.0, 4.0] units, respectively. The limit of agreement was reduced from [-3.6, 5.3], and [-3.6, 4.2] to [-0.5, 3.0], and [-1.3, 1.6] for CI and CVAI, respectively, by combining results from different camera angles, and positions in our method. The overall accuracy of the proposed technology for DPB detection was 100%. Conclusions: Photographic 2D images can be accurately analyzed to assess DPB at the point-of-care. By compensating for the error from variable camera angles, and distance from the head, our technology eliminates user variability. The algorithms will be packaged in a mobile application to enable the use of the technology at the point-of-care.
