Structural Deformations in Cucurbit[n]urils: Analysis, Host-Guest Dependence, and Automated Ellipticity Measurements Using ElliptiCB[n].

Cucurbit[n]urils (CB[n]s) are cyclic macrocycles with rich host-guest chemistry. In many cases, guest binding in CB[n]s results in host structural deformations. Unfortunately, measuring such deformations remains a major challenge, with only a handful of manual estimations reported in the literature. To address this challenge, we have developed the public program ElliptiCB[n], which is available on GitHub, that provides a robust and automated method for measuring the elliptical deformations in CB[n] hosts. We outline the development and validation of this approach, apply ElliptiCB[n] to measure the ellipticity of the 1113 available CB[n] structures from the Cambridge Structural Database (CSD), and directly investigate the structural deformations of CB[5], CB[6], CB[7], CB[8], and CB[10] hosts. We also report the general landscape of accessible CB[n] elliptical deformations and compare ellipticity distributions across CB[n] hosts and host-guest complexes. We found that in almost all cases guest binding significantly impacts the distribution of host ellipticity distributions and that these distributions are dissimilar across host-guest complexes of differently sized CB[n]s. We anticipate that this work will provide a useful approach for understanding of the flexibility of CB[n] hosts and will also enable future measurement and standardization of ellipticity measurements of CB[n]s.

A systematic review and meta-analysis of correlation of automated breast density measurement.

INTRODUCTION: Breast cancer is the most common cancer in women and a leading cause of mortality. This systematic review and meta-analysis aims to evaluate the correlation between breast density measurements obtained from various software and visual assessments by radiologists using full-field digital mammography (FFDM). METHODS: Following the PRISMA 2020 guidelines, five databases (Pubmed, Google Scholar, Science Direct, Cochrane Library, and MEDLINE) were searched for studies correlating volumetric breast density with breast cancer risk. The Newcastle-Ottawa Scale and the Joanna Briggs Institute Checklist were used to assess the quality of the included studies. Meta-analysis of correlation was applied to aggregate correlation coefficients using a random-effects model using MedCalc Statistical Software version 19.2.6. RESULTS: The review included 22 studies with a total of 58,491 women. The pooled correlation coefficient for volumetric breast density amongst Volpara™ and Quantra™ was found to be 0.755 (95% CI 0.496-0.890, p < 0.001), indicating a high positive correlation, albeit with a significant heterogeneity (I2 = 99.89%, p < 0.0001). Subgroup analyses based on study origin, quality, and methodology were performed but did not reveal the heterogeneity cause. Egger's and Begg's tests showed no significant publication bias. CONCLUSION: Volumetric breast density is strongly correlated with breast cancer risk, underscoring the importance of accurate breast density assessment in screening programs. Automated volumetric measurement tools like Volpara™ and Quantra™ provide reliable assessments, potentially improving breast cancer risk prediction and management. IMPLICATIONS FOR PRACTICE: Implementing fully automated breast density assessment tools could enhance consistency in clinical practice, minimizing observer variability and improving screening accuracy. These tools should be further validated against standardized criteria to ensure reliability in diverse clinical settings.

Putting the child in the driver's seat: Insights into language development from children's interactions in preschool classrooms.

Children's own language production has a role in structuring the language of their conversation partners and influences their own development. Children's active participation in their own language development is most apparent in the rich body of work investigating language in natural environments. The advent of automated measures of vocalizations and movement have made such in situ research increasingly feasible. In this chapter, we review recent research on children's language development in context with a particular focus on research employing automated methods in preschool classrooms for children between ages 2 and 5 years. These automated methods indicate that the speech directed to preschool children from specific peers predicts the child's speech to those peers on a subsequent observation occasion. Similar patterns are seen in the influence of peer and teacher phonemic diversity on the phonemic diversity of children's speech to those partners. In both cases, children's own speech to partners was the best predictor of their language abilities, suggesting their active role in their own development. Finally, new research suggests the potential of machine learning to predict children's speech in group contexts, and to transcribe classroom speech to better understand the content of children's conversations and how they change with development.

Artificial intelligence-based assessment of leg axis parameters shows excellent agreement with human raters: A systematic review and meta-analysis.

PURPOSE: The aim of this study was to conduct a systematic review and meta-analysis on the reliability and applicability of artificial intelligence (AI)-based analysis of leg axis parameters. We hypothesized that AI-based leg axis measurements would be less time-consuming and as accurate as those performed by human raters. METHODS: The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO). PubMed, Epistemonikos, and Web of Science were searched up to 24 February 2024, using a BOOLEAN search strategy. Titles and abstracts of identified records were screened through a stepwise process. Data extraction and quality assessment of the included papers were followed by a frequentist meta-analysis employing a common effect/random effects model with inverse variance and the Sidik-Jonkman heterogeneity estimator. RESULTS: A total of 13 studies encompassing 3192 patients were included in this meta-analysis. All studies compared AI-based leg axis measurements on long-leg radiographs (LLR) with those performed by human raters. The parameters hip knee ankle angle (HKA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibial angle (mMPTA), and joint-line convergence angle (JLCA) showed excellent agreement between AI and human raters. The AI system was approximately 3 min faster in reading standing long-leg anteroposterior radiographs (LLRs) compared with human raters. CONCLUSION: AI-based assessment of leg axis parameters is an efficient, accurate, and time-saving procedure. The quality of AI-based assessment of the investigated parameters does not appear to be affected by the presence of implants or pathological conditions. LEVEL OF EVIDENCE: Level I.

Evaluation of a Semi-Automated Wound-Halving Algorithm for Split-Wound Design Studies: A Step towards Enhanced Wound-Healing Assessment.

Background: Chronic leg ulcers present a global challenge in healthcare, necessitating precise wound measurement for effective treatment evaluation. This study is the first to validate the "split-wound design" approach for wound studies using objective measures. We further improved this relatively new approach and combined it with a semi-automated wound measurement algorithm. Method: The algorithm is capable of plotting an objective halving line that is calculated by splitting the bounding box of the wound surface along the longest side. To evaluate this algorithm, we compared the accuracy of the subjective wound halving of manual operators of different backgrounds with the algorithm-generated halving line and the ground truth, in two separate rounds. Results: The median absolute deviation (MAD) from the ground truth of the manual wound halving was 2% and 3% in the first and second round, respectively. On the other hand, the algorithm-generated halving line showed a significantly lower deviation from the ground truth (MAD = 0.3%, p < 0.001). Conclusions: The data suggest that this wound-halving algorithm is suitable and reliable for conducting wound studies. This innovative combination of a semi-automated algorithm paired with a unique study design offers several advantages, including reduced patient recruitment needs, accelerated study planning, and cost savings, thereby expediting evidence generation in the field of wound care. Our findings highlight a promising path forward for improving wound research and clinical practice.

Automation improves the efficiency of weightbearing CT scan 3D volumetric assessments of the syndesmosis.

BACKGROUND: Weight-bearing CT (WBCT) 3D volumetric measurement has shown promising accuracy for the diagnosis of syndesmotic instability. However, these measurements are rather complex and time-consuming, rendering them a clinically unfavorable option. We hypothesized that automatized measurements would be more accurate and time-efficient than manual ones. METHODS: Thirty cases of intraoperatively confirmed syndesmotic instability along with thirty individuals with no injuries to the ankle joint were recruited as cases and controls, retrospectively. Two observers conducted the manual volumetric measurements two times, at a one-week interval. An automated algorithm for 3D WBCT measurements was developed to conduct the measurements on the axial images. The time spent on each method was recorded. Mann-Whitney U test was used to compare the values between human raters and computers. Inter- and intra-class reliability were calculated. RESULTS: The intra-class correlation coefficient was found to be "excellent" for the automated measurements (0.97) and "good" for the observers (0.75). Similarly, the Cronbach's alpha was shown to be higher for the computer (0.88) than the observers (0.60 and 0.62). The mean time spent on the measurements was different between human raters and the computer-assisted method (p < 0.001). CONCLUSION: Automated volumetric assessment of syndesmosis seems to be a faster and more reliable option than the manual one. We suggest future larger-scale prospective studies conducted under actual clinical circumstances for more definitive conclusions. LEVEL OF EVIDENCE: Retrospective case-control study - Level 3.

Development of a semi-automated tool to measure fetal abdominal wall thickness during ultrasound at 20 weeks' gestation.

OBJECTIVES: To develop a semi-automated tool for measuring fetal abdominal wall thickness (AWT). To validate the software using images captured by other centers and create a nomogram for fetal AWT between 18 and 20 weeks. METHODS: A semiautomated tool that measured AWT was developed using images captured at the routine 20-week morphology scan. The software was developed using digital images captured routinely during scans of low-risk women. Inter- and intraobserver reliability was assessed between manual and semi-automated measures. The tool was validated using images acquired from other centers. Linear regression and quadratic polynomials were used to create a nomogram for AWT. RESULTS: The semi-automated tool was able to measure AWT in all images. Interoperator reliability was 0.90 and 0.97 (P < 0.05) for manual and semi-automated methods, respectively. Measurement agreement varied between three operators from moderate to excellent (0.77, 0.87, 0.92), with overall agreement being good (0.85). The tool could be successfully applied to 89% of images from other centers. A nomogram was generated for AWT measurements of fetuses at 18-20 weeks in normal, low risk mothers. CONCLUSION: Semi-automated measurement of AWT was feasible using images captured during the routine 20-week scan. This approach had lower inter- and intraobserver variability compared to manual measurement.

Population data-based federated machine learning improves automated echocardiographic quantification of cardiac structure and function: the Automatisierte Vermessung der Echokardiographie project.

Aims: Machine-learning (ML)-based automated measurement of echocardiography images emerges as an option to reduce observer variability. The objective of the study is to improve the accuracy of a pre-existing automated reading tool ('original detector') by federated ML-based re-training. Methods and results: Automatisierte Vermessung der Echokardiographie was based on the echocardiography images of n = 4965 participants of the population-based Characteristics and Course of Heart Failure Stages A-B and Determinants of Progression Cohort Study. We implemented federated ML: echocardiography images were read by the Academic Core Lab Ultrasound-based Cardiovascular Imaging at the University Hospital Würzburg (UKW). A random algorithm selected 3226 participants for re-training of the original detector. According to data protection rules, the generation of ground truth and ML training cycles took place within the UKW network. Only non-personal training weights were exchanged with the external cooperation partner for the refinement of ML algorithms. Both the original detectors as the re-trained detector were then applied to the echocardiograms of n = 563 participants not used for training. With regard to the human referent, the re-trained detector revealed (i) superior accuracy when contrasted with the original detector's performance as it arrived at significantly smaller mean differences in all but one parameter, and a (ii) smaller absolute difference between measurements when compared with a group of different human observers. Conclusion: Population data-based ML in a federated ML set-up was feasible. The re-trained detector exhibited a much lower measurement variability than human readers. This gain in accuracy and precision strengthens the confidence in automated echocardiographic readings, which carries large potential for applications in various settings.

Remote and automated high-throughput powder diffraction measurements enabled by a robotic sample changer at SSRL beamline 2-1.

The general-purpose powder diffractometer beamline (BL2-1) at the Stanford Synchrotron Radiation Lightsource (SSRL) is described. The evolution of design and performance of BL2-1 are presented, in addition to current operating specifications, applications and measurement capabilities. Recent developments involve a robotic sample changer enabling high-throughput X-ray diffraction measurements, applicable to mail-in and remote operations. In situ and operando capabilities to measure samples with different form factors (e.g. capillary, flat plate or thin film, and transmission) and under variable experimental conditions are discussed. Several example datasets and accompanying Rietveld refinements are presented.

B3X: a novel efficient algorithm for accurate automated auscultatory blood pressure estimation.

Objective.The auscultatory technique is still considered the most accurate method for non-invasive blood pressure (NIBP) measurement, although its reliability depends on operator's skills. Various methods for automated Korotkoff sounds analysis have been proposed for reliable estimation of systolic (SBP) and diastolic (DBP) blood pressures. To this aim, very complex methodologies have been presented, including some based on artificial intelligence (AI). This study proposes a relatively simple methodology, named B3X, to estimate SBP and DBP by processing Korotkoff sounds recordings acquired during an auscultatory NIBP measurement.Approach.The beat-by-beat change in morphology of adjacent Korotkoff sounds is evaluated via their cross-correlation. The time series of the beat-by-beat cross-correlation and its first derivative are analyzed to locate the timings of SBP and DBP values. Extensive tests were performed on a public database of 350 annotated measurements, and the performance was evaluated according to the BHS, AAMI/ANSI, and International Organization for Standardization (ISO) quality standards.Main results.The proposed approach achieved 'A' scores for SBP and DBP in the BHS grading system, and passed the quality tests of AAMI/ANSI and ISO standards. The B3X algorithm outperformed two well-established algorithms for oscillometric NIBP measurement in both SBP and DBP estimation. It also outperformed four AI-based algorithms in DBP estimation, while providing comparable performance for SBP, at the cost of a much lower computational burden. The full code of the B3X algorithm is provided in a public repository.Significance.The very good performances ensured by the proposed B3X algorithm, at a low computational cost and without the need for parameter training, support its direct implementation into clinical blood pressure (BP) monitoring devices. The results of this study pave the way for solving/overcoming the trade-off between the accuracy of the auscultatory technique and the objectivity of oscillatory measurements, by bringing an automated auscultatory BP measurement method in clinical practice.

Towards Automated Measurement of As-Built Components Using Computer Vision.

Regular inspections during construction work ensure that the completed work aligns with the plans and specifications and that it is within the planned time and budget. This requires frequent physical site observations to independently measure and verify the completion percentage of the construction progress performed over periods of time. The current computer vision techniques for measuring as-built elements predominantly employ three-dimensional laser scanning or three-dimensional photogrammetry modeling to ascertain the geometric properties of as-built elements on construction sites. Both techniques require data acquisition from several positions and angles to generate sufficient information about the element's coordinates, making the deployment of these techniques on dynamic construction project sites challenging. This paper proposes a pipeline for automating the measurement of as-built components using artificial intelligence and computer vision techniques. The pipeline requires a single image obtained with a stereo camera system to measure the sizes of selected objects or as-built components. The results in this work were demonstrated by measuring the sizes of concrete walls and columns. The novelty of this work is attributed to the use of a single image and a single target for developing a fully automated computer vision-based method for measuring any given object. The proposed solution is suitable for use in measuring the sizes of as-built components in built assets. It has the potential to be further developed and integrated with building information modelling applications for use on construction projects for progress monitoring.

Automated 2-Dimensional Measurement of Vestibular Schwannoma: Validity and Accuracy of an Artificial Intelligence Algorithm.

OBJECTIVE: Validation of automated 2-dimensional (2D) diameter measurements of vestibular schwannomas on magnetic resonance imaging (MRI). STUDY DESIGN: Retrospective validation study using 2 data sets containing MRIs of vestibular schwannoma patients. SETTING: University Hospital in The Netherlands. METHODS: Two data sets were used, 1 containing 1 scan per patient (n = 134) and the other containing at least 3 consecutive MRIs of 51 patients, all with contrast-enhanced T1 or high-resolution T2 sequences. 2D measurements of the maximal extrameatal diameters in the axial plane were automatically derived from a 3D-convolutional neural network compared to manual measurements by 2 human observers. Intra- and interobserver variabilities were calculated using the intraclass correlation coefficient (ICC), agreement on tumor progression using Cohen's kappa. RESULTS: The human intra- and interobserver variability showed a high correlation (ICC: 0.98-0.99) and limits of agreement of 1.7 to 2.1 mm. Comparing the automated to human measurements resulted in ICC of 0.98 (95% confidence interval [CI]: 0.974; 0.987) and 0.97 (95% CI: 0.968; 0.984), with limits of agreement of 2.2 and 2.1 mm for diameters parallel and perpendicular to the posterior side of the temporal bone, respectively. There was satisfactory agreement on tumor progression between automated measurements and human observers (Cohen's κ = 0.77), better than the agreement between the human observers (Cohen's κ = 0.74). CONCLUSION: Automated 2D diameter measurements and growth detection of vestibular schwannomas are at least as accurate as human 2D measurements. In clinical practice, measurements of the maximal extrameatal tumor (2D) diameters of vestibular schwannomas provide important complementary information to total tumor volume (3D) measurements. Combining both in an automated measurement algorithm facilitates clinical adoption.

Evaluation of a novel moving threshold gating strategy for assessment of reticulated platelets in dogs using the ADVIA 2120 analyzer.

BACKGROUND: A novel method using a moving threshold (r-PLTmt) to determine canine r-PLTs (reticulated platelets) has been introduced for ADVIA 2120 software v6.11.7. OBJECTIVES: We aimed to evaluate absolute (ar-PLTmt) and percent (%r-PLTmt) prior to and after visual inspection of scattergrams (ar-PLTmtv, %rPLTmtv) compared with flow cytometry (flow) and to determine reference intervals (RIs) in 120 dogs. METHODS: For method comparison, 42 blood samples of healthy and thrombocytopenic dogs were included. Calculation of Spearman's rho, Bland-Altman, and Passing-Bablok analysis was performed. Coefficients of variation (CVs) were determined for three concentration levels. RESULTS: Moderate correlations between %r-PLTmt and %r-PLTmtv (rs 0.75-0.76) were seen compared with flow cytometry. The CV for medium %r-PLTs counts assessed with flow cytometry was 12.9%. Comparable CVs were obtained for ar-PLTmt (14.4%) and %r-PLTmt (15.7%), and ar-PLTmtv and %r-PLTmtv (10.9% and 12.9%, respectively). At low and high concentration levels, CVs for % and absolute r-PLTmt/rPLTmtv ranged between 23%-30% and 15%-20%. In patients with microcytic hypochromic erythrocytes, CVs for ar-PLTmt and %r-PLTmt were 36%-66%. Visual inspection of scattergrams resulted in a marked decrease in CV ranging between 15% and 20%. A proportional bias of 10.8% between %r-PLTmt and flow cytometry became lower (9.7%) after visual validation of scattergrams. Passing-Bablok analysis showed proportional and constant error. RIs for r-PLTmt and r-PLTmtv were 0.2%-3.8% and 0.6-10.2 × 109 /L and 0.3%-4.5% and 1.1-10.3 × 109 /L, respectively. Median values for %r-PLTmtv were higher in young adults (≤2 years) than in older dogs (P = 0.03). CONCLUSIONS: r-PLTmt and r-PLTmtv were moderately correlated with flow cytometry. Visual inspection of scattergrams is recommended.

Smart Hip Ultrasound Technology in Screening for Infant Developmental Dysplasia of the Hip / 中山大学学报(医学科学版)

ObjectiveTo develop a deep learning system for early ultrasound screening of developmental dysplasia of the hip （DDH）， a new smart-hip ultrasound technique （S-hip）， and to validate its clinical application. MethodsWe selected 11，100 annotated and reviewed coronal ultrasound images of infant hips between November 2021 and August 2022， 8，100 of which were used for the training set and 3，000 for the test set， to build a S-hip deep learning system. To verify the consistency between the automated measurement by S-hip and the manual measurements by sonographers， 174 standard coronal ultrasound images of 87 infants' bilateral hips were acquired， then α angle， β angle and femoral head coverage （FHC） were measured by S-hip， an ultrasound expert and a resident. The measurement data and the time required for the measurements were recorded and statistically analyzed. Another 100 standard coronal ultrasound images of the hips were randomly selected and measured twice respectively by the ultrasound expert and resident to assess the intra-sonographer repeatability. ResultsThe intraclass correlation coefficient （ICC） （95% CI） values of α angle， β angle and FHC results measured by S-hip and ultrasound expert were 0.799 （0.738， 0.847）， 0.798 （0.737， 0.846） and 0.934 （0.954， 0.975）， respectively. Those values measured by the ultrasound expert and resident were 0.725 （0.645， 0.789）， 0.674 （0.583， 0.748） and 0.931 （0.908， 0.949）， respectively. The mean absolute errors （MAE） of α angle， β angle and FHC results between measurements by S-hip and ultrasound expert were 2.69 °， 4.43 ° and 2.47%， respectively. The time required for measurements by S-hip， ultrasound expert and resident was （1.59±0.36） s， （18.76±2.23） s and （19.45±2.76） s， respectively. The automated measurement by S-hip cost much shorter time than the manual measurements by sonographers and the difference was statistically significant （P<0.001）. The ICC （95% CI） values of α angle， β angle and FHC results between two measurements by the ultrasound expert were 0.943 （0.916， 0.961）， 0.959 （0.940， 0.972）， and 0.981 （0.971， 0.987）， respectively. Those values by the ultrasound resident were 0.884 （0.833， 0.921）， 0.921 （0.884， 0.946）， and 0.962 （0.944， 0.974）. ConclusionThe S-hip based on a deep learning system is a highly reliable automated technique to accurately measure α angle， β angle and FHC. Compared with ultrasound residents， S-hip allows for a more simplified and significantly quicker measurement， which may enhance the widespread use of hip ultrasound screening in infants.

Novel method for the quantification of rosette area from images of Arabidopsis seedlings grown on agar plates.

Premise: The agar-based culture of Arabidopsis seedlings is widely used for quantifying root traits. Shoot traits are generally overlooked in these studies, probably because the rosettes are often askew. A technique to assess the shoot surface area of seedlings grown inside agar culture dishes would facilitate simultaneous root and shoot phenotyping. Methods: We developed an image processing workflow in Python that estimates rosette area of Arabidopsis seedlings on agar culture dishes. We validated this method by comparing its output with other metrics of seedling growth. As part of a larger study on genetic variation in plant responses to nitrogen form and concentration, we measured the rosette areas from more than 2000 plate images. Results: The rosette area measured from plate images was strongly correlated with the rosette area measured from directly overhead and moderately correlated with seedling mass. Rosette area in the large image set was significantly influenced by genotype and nitrogen treatment. The broad-sense heritability of leaf area measured using this method was 0.28. Discussion: These results indicated that this approach for estimating rosette area produces accurate shoot phenotype data. It can be used with image sets for which other methods of leaf area quantification prove unsuitable.

Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion-neutral-extension cervical lateral radiographs.

BACKGROUND: The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few investigations on spinal motion analysis. The purpose of this study is to develop a deep learning-based model for fully automated measurement of SIRM based on flexion-neutral-extension cervical lateral radiographs and to evaluate its applicability for the flexion-extension (F/E), flexion-neutral (F/N), and neutral-extension (N/E) motion analysis. METHODS: A total of 2796 flexion, neutral, and extension cervical lateral radiographs from 932 patients were analyzed. Radiographs from 100 patients were randomly selected as the test set, and those from the remaining 832 patients were used for training and validation. Landmarks were annotated for measuring SIRM at five segments from C2/3 to C6/7 on F/E, F/N, and N/E motion. High-Resolution Net (HRNet) was used as the main structure to train the landmark detection network. Landmark performance was assessed according to the percentage of correct key points (PCK) and mean of the percentage of correct key points (MPCK). Measurement performance was evaluated by intra-class correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots. RESULTS: At a 2-mm distance threshold, the PCK for the model ranged from 94 to 100%. Compared with the reference standards, the model showed high accuracy for SIRM measurements for all segments on F/E and F/N motion. On N/E motion, the model provided reliable measurements from C3/4 to C6/7, but not C2/3. Compared with the radiologists' measurements, the model showed similar performance to the radiologists. CONCLUSIONS: The developed model can automatically measure SIRM on flexion-neutral-extension cervical lateral radiographs and showed comparable performance with radiologists. It may provide rapid, accurate, and comprehensive information for cervical motion analysis.

Medición automática de la zona avascular foveal de ojos sanos en angiografía por tomografía de coherencia óptica de dominio espectral Heidelberg / Automated measurement of the foveal avascular zone in healthy eyes on Heidelberg spectralis optical coherence tomography angiography

Objetivos Desarrollar y evaluar un método automático de medición del área de la zona avascular foveal (ZAF) en ojos sanos en angiografía por tomografía de coherencia óptica de dominio espectral Heidelberg (HS-OCTA). Este método se denomina Macro Kanno-Saitama modificado (KSMm) y es una evolución de la aproximación Macro Kanno-Saitama (KSM). Métodos Este estudio transversal incluyó 29 ojos de 25 voluntarios sanos a los que se les realizaron dos HS-OCTA de la zona macular en el mismo momento. Se incluyeron todas las imágenes a pesar de su calidad. Los datos de la zona macular del plexo vascular superficial, del plexo capilar intermedio (PCI) y del plexo capilar profundo fueron procesados usando KSMm. El área ZAF se midió dos veces automáticamente usando KSMm y KSM y dos veces manualmente por dos examinadores independientes. Resultados De 174 imágenes, KSM no pudo medir correctamente el 31%, mientras que KSMm pudo medir con éxito todas las imágenes. El coeficiente intraclase intraescaneo varió entre 0,948 y 0,993 para medidas manuales y fue 1 con el método KSMm. Aunque según los gráficos de Bland-Altman la diferencia entre examinadores humanos es menor que entre examinadores humanos y KSMm, los gráficos de dispersión muestran una fuerte correlación entre las medidas realizadas por humanos y las automáticas. Los mejores resultados se obtienen en PCI. Conclusiones Usando KSMm, la medida automática del área ZAF en HS-OCTA es factible y menos humano-dependiente. Resuelve la incapacidad de KSM de medir el área ZAF en imágenes de calidad subóptima que son frecuentes en la práctica clínica diaria. Por lo tanto, el procesamiento KSMm podría contribuir a nuestra comprensión de los tres plexos vasculares (AU)

Purpose To develop and evaluate an automated method to measure the foveal avascular zone (FAZ) area in healthy eyes on Heidelberg Spectralis Optical Coherence Tomography Angiography (HS-OCTA). This method is referred to as the modified Kanno-Saitama macro (mKSM) which is an evolution of the Kanno-Saitama macro (KSM) approach. Methods This cross-sectional study included 29 eyes of 25 healthy volunteers who underwent HS-OCTA at the macular area twice at the same time. Regardless of the quality of the images, all of them were included. Macular data on the superficial vascular plexus, intermediate capillary plexus (ICP) and deep capillary plexus were processed by mKSM. The FAZ area was measured twice automatically using the mKSM and KSM and twice manually by two independent examiners. Results From 174 images, KSM could not measure correctly 31% while mKSM could successfully measure all of them. Intrascan intraclass coefficient ranged from 0.948 to 0.993 for manual measurements and was 1 for mKSM method. Despite that the difference between human examiners is smaller than between human examiners and mKSM according to Bland-Altman plots, the scatterplots show a strong correlation between human and automatic measurements. The best results are obtained in ICP. Conclusions With mKSM, the automated determination of the FAZ area in HS-OCTA is feasible and less human-dependent. It solves the inability of KSM to measure the FAZ area in suboptimal quality images which are frequent in daily clinical practice. Therefore, the mKSM processing could contribute to our understanding of the three vascular plexuses (AU)

Automated measurement of the foveal avascular zone in healthy eyes on Heidelberg spectralis optical coherence tomography angiography.

PURPOSE: To develop and evaluate an automated method to measure the foveal avascular zone (FAZ) area in healthy eyes on Heidelberg Spectralis Optical Coherence Tomography Angiography (HS-OCTA). This method is referred to as the modified Kanno-Saitama macro (mKSM) which is an evolution of the Kanno-Saitama macro (KSM) approach. METHODS: This cross-sectional study included 29 eyes of 25 healthy volunteers who underwent HS-OCTA at the macular area twice at the same time. Regardless of the quality of the images, all of them were included. Macular data on the superficial vascular plexus, intermediate capillary plexus (ICP) and deep capillary plexus were processed by mKSM. The FAZ area was measured twice automatically using the mKSM and KSM and twice manually by two independent examiners. RESULTS: From 174 images, KSM could not measure correctly 31% while mKSM could successfully measure all of them. Intrascan intraclass coefficient ranged from 0,948 to 0,993 for manual measurements and was 1 for mKSM method. Despite that the difference between human examiners is smaller than between human examiners and mKSM according to Bland-Altman plots, the scatterplots show a strong correlation between human and automatic measurements. The best results are obtained in ICP. CONCLUSIONS: With mKSM, the automated determination of the FAZ area in HS-OCTA is feasible and less human-dependent. It solves the inability of KSM to measure the FAZ area in suboptimal quality images which are frequent in daily clinical practice. Therefore, the mKSM processing could contribute to our understanding of the three vascular plexuses.