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Un nombre significatif d’enfants autistes ont une macrocéphalie. Malgré plusieurs études du périmètre crânien en autisme, peu d’études ont été faites sur des adultes. Aussi, les références actuelles en périmètre crânien (PC) adulte datent d’environ 20 ans. Les objectifs de cette étude étaient de construire une échelle de référence du PC adulte, et de comparer les taux de macrocéphalie entre un groupe d’adultes autistes et un groupe d’adultes neurotypiques. Dans cette étude, 221 sujets masculins adultes étaient recrutés de différents milieux afin de déterminer le meilleur modèle prédictif du PC et de construire l’échelle de référence. La hauteur et le poids étaient mesurés pour chaque participant afin de déterminer leur influence sur les dimensions crâniennes. Pour la partie comparative, 30 autistes et 36 sujets neurotypiques, tous adultes, étaient recrutés à partir de la base de données du laboratoire de recherche. Pour l’échelle de référence, les résultats démontraient des corrélations positives entre le PC avec la hauteur et le poids. Après analyse, la corrélation conjointe de la hauteur et du poids sur le PC a été déterminée comme étant le modèle qui offre les résultats les plus significatifs dans la prédiction du PC. Pour la partie comparative, les taux de macrocéphalie atteignaient 10,00% chez les autistes contre 2,56% chez les neurotypiques selon la formule de régression linéaire obtenue du modèle. Cependant le test d’exactitude de Fisher n’a révélé aucune différence significative entre les 2 groupes. Mes résultats suggèrent qu’il est nécessaire de considérer la hauteur et le poids en construisant une référence du PC et que, même en utilisant la nouvelle référence, les taux de macrocéphalie demeurent plus élevés chez les autistes adultes que chez les adultes neurotypiques en dépit de l’absence de différences significatives. / A significant proportion of autistic children have macrocephaly. Despite numerous head circumference studies in autism, few studies in adults exist. Also, current adult head circumference (HC) references are around 20 years old. The purposes of this study were to construct an adult head measurement reference chart, and to compare macrocephaly rates between a group of autistic adults and a group of typically developing adults. In this study, 221 male adults were recruited from various settings in order to determine the best predictive model for HC and to construct the reference chart. Height and weight were measured for each participant in order to evaluate their influence on head dimensions. For the comparison part, 30 autistic and 36 typically developing adult subjects were recruited from within the research lab’s database. For the reference chart, results showed positive correlations between HC, and both height and weight. After analysis, the combined influence of both height and weight on HC size has was determined to be the model showing most significant results in predicting HC. For the comparison part, macrocephaly rates reached 10.00% in the autistic group against 2.56% in the control group according to the linear regression formula obtained from the model. However, the Fisher’s exact test revealed no significant difference in macrocephaly rates between both groups. My results suggest the necessity of considering both height and weight when constructing a head circumference reference chart and that, even using a new reference, macrocephaly rates remain higher than normal in autistic adults although not significantly different enough from the typically developing adults.

<p dir="ltr">Blood hemoglobin (Hgb) testing is a widely used clinical laboratory test for a variety of patient care needs. However, conventional blood Hgb measurements involve invasive blood sampling, exposing patients to potential risks and complications from needle pricks and iatrogenic blood loss. Although noninvasive blood Hgb quantification methods are under development, they still pose challenges in achieving performance comparable to clinical laboratory blood Hgb test results (i.e., gold standard). In particular, optical spectroscopy can provide reliable blood Hgb tests, but its practical utilizations in diagnostics are limited by bulky optical components, high costs, and extended data acquisition time. Mobile health (mHealth) or diagnostic colorimetric applications have a potential for point-of-care blood Hgb testing. However, achieving color accuracy for diagnostic applications is a complex matter, affected by device models, light conditions, and image file formats.</p><p dir="ltr">To address these limitations, we propose biophysics-based machine learning algorithms that combine hyperspectral learning and spectroscopic gamut-informed learning for accurate and precise mHealth blood Hgb assessments in a noninvasive manner. This method utilizes single-shot photographs of peripheral tissue acquired by onboard smartphone cameras. The palpebral conjunctiva (i.e., inner eyelid) serves as an ideal peripheral tissue site, owing to its easy accessibility, relatively uniform microvasculature, and absence of skin pigmentation (i.e., melanocytes). First, hyperspectral learning enables a mapping from red-green-blue (RGB) values of a digital camera into detailed hyperspectral information: an inverse mapping from a sparse space (tristimulus color values) to a dense space (multiple wavelengths). Hyperspectral learning employs a statistical learning framework to reconstruct a high-resolution spectrum from a digital photo of the palpebral conjunctiva, eliminating the need for complex and costly optical instrumentation. Second, comprehensive spectroscopic analyses of peripheral tissue are used to establish a unique blood Hgb gamut and design a diagnostic color reference chart highly sensitive to blood Hgb and peripheral perfusion. Informed by the domain knowledge of tissue optics and machine vision, the Hgb gamut-based learning algorithm offers device/light/format-agnostic color recovery of the palpebral conjunctiva, outperforming the existing color correction methods.</p><p dir="ltr">This mHealth blood Hgb prediction method exhibits comparable accuracy and precision to capillary blood sampling tests (e.g., finger prick) over a wide range of blood Hgb values, ensuring its reliability, consistency, and reproducibility. Importantly, by employing only a digital photograph with the Hgb gamut-learned color recovery, hyperspectral learning-based blood Hgb assessments allow noninvasive, continuous, and real-time reading of blood Hgb levels in resource-limited and at-home settings. Furthermore, our biophysics-based machine learning approaches for digital health applications can lay the foundation for the future of personalized medicine and facilitate the tempo of clinical translation, empowering individuals and frontline healthcare workers.</p>