Parametric imaging of attenuation by optical coherence tomography: review of models, methods, and clinical translation

SIGNIFICANCE: Optical coherence tomography (OCT) provides cross-sectional and volumetric images of backscattering from biological tissue that reveal the tissue morphology. The strength of the scattering, characterized by an attenuation coefficient, represents an alternative and complementary tissue optical property, which can be characterized by parametric imaging of the OCT attenuation coefficient. Over the last 15 years, a multitude of studies have been reported seeking to advance methods to determine the OCT attenuation coefficient and developing them toward clinical applications. AIM: Our review provides an overview of the main models and methods, their assumptions and applicability, together with a survey of preclinical and clinical demonstrations and their translation potential. RESULTS: The use of the attenuation coefficient, particularly when presented in the form of parametric en face images, is shown to be applicable in various medical fields. Most studies show the promise of the OCT attenuation coefficient in differentiating between tissues of clinical interest but vary widely in approach. CONCLUSIONS: As a future step, a consensus on the model and method used for the determination of the attenuation coefficient is an important precursor to large-scale studies. With our review, we hope to provide a basis for discussion toward establishing this consensus

Limitations of Weight Velocity Analysis by Commercial Computer Program Growth Analyser Viewer Edition

Commercial software package “Growth Analyser Viewer Edition” (“GAVE”) aims to document, monitor and analyze growth and development in children and adolescents. Although its clinical and scientific use is widespread, there are no published studies that describe the method and its validation. We were informed that GAVE calculates the weight velocity (kg/year) at age t from the weight difference between t and 448 days earlier or at birth, divided by the time difference. We recently discussed a case of false child abuse diagnosis (Pediatric Condition Falsification), resulting in the separation of the child from its parents, in which GAVE played a negative contributing role. To prevent such inappropriate diagnoses, we analyzed GAVE from a schematic representation of the measured clinical weight curve, with precisely defined weight velocities. In conclusion, the 448 days included for weight velocity predictions by GAVE caused the erroneous outcomes. Until the necessary changes to the software are implemented and validated, we advise against the use of GAVE in infants younger than 1.5 years, if multiple weight changes occur within 448 days, and following a long-lasting weight velocity change. Our analysis suggests to discard all medical software packages that lack public description and proof of validation

Weight velocity equations with 14–448 days time separated weights should not be used for infants under 3 years of age

Abnormal growth of infants may indicate disease of the children, thus methods to identify growth disorders are wanted in medicine. We previously showed that two-time-points weight growth velocities at age t, calculated by a commercial software product as [Weight(t)− Weight(t − X)]/X, with X = 448 days, were erroneous due to the long separation of 448 days. We were convinced that shorter X-values would solve this accuracy problem. However, our hypothesis is that: “shorter time separations than 448 days cause a decreased accuracy of numerical weight velocity equations in realistic infant weights until an age of about three years”. Supporting evidence comes from analyzing how shorter X-values will affect the accuracy of two-time-points weight velocity calculations. We systematically varied X between 1 and 448 days of various P50/0SD-related standard weight curves: (a)P50/0SD with the weights separated by 1 day and X = 1,28,224,448 days; (b)P50/0SD with the weights at variable ages and X = 14–448 days; and (c)case (b)and incorporating weight fluctuations typically occurring in infants. Cases (b)and (c)include details observed in a clinical case. Our results show that the combination of weight fluctuations and varying time intervals between consecutive weights make weight velocity predictions worse for shorter X values in children younger than three years. Because these two causes of failure occur naturally in infants whose weight is regularly measured, other weight velocity equations face the same causes for inaccuracy. In conclusion, our hypothesis suggests that any software that predicts weight velocities should be abandoned in infants < 3 years. Practically, it should require that when (commercial)software weight velocity prediction suggests a medical problem, careful clinical checking should be mandatory, e.g. by linking predicted and exact weight velocities at age t (the latter from the mathematical first derivative at age t of standard weight curves)