Visual Assessment Research Articles

The utility and reliability of the PreViAs questionnaire forthe assessment of vision in Turkish neonates and infants.

Introduction: Evaluating the visual functions of children with an easy-to-use and evidence-based method during the preverbal period will enable early diagnosis and intervention of visual impairments. The aim of this study is to determine the utility and reliability of the Turkish version of the Preverbal Visual Assessment (PreViAs) questionnaire, which was developed to evaluate the visual functioning of preverbal infants. Population and Methods: The PreViAs questionnaire was administered to primary caregivers of term infants under 24 months of age, and their responses were recorded. Results: Data from the 278 participating infants were analyzed to assess the internal consistency of the PreViAs questionnaire. Results showed a high level of consistency with Cronbach's alpha value of 0.958 for the total score, suggesting strong internal coherence. In addition, the Cronbach's alpha values for each domain were 0.890, 0.913, 0.951, and 0.922 for visual attention, visual communication, visual processing, and visual-motor coordination, respectively, indicating good internal consistency for each subdomain. Conclusion: The Turkish version of the PreViAs questionnaire is useful and reliable for assessing functional vision during the preverbal period.

Digital "flipbooks" for enhanced visual assessment of simple and complex brain tumors.

Typical longitudinal radiographic assessment of brain tumors relies on side-by-side qualitative visualization of serial magnetic resonance images (MRIs) aided by quantitative measurements of tumor size. However, when assessing slowly-growing tumors and/or complex tumors, side-by-side visualization and quantification may be difficult or unreliable. Whole-brain, patient-specific "digital flipbooks" of longitudinal scans are a potential method to augment radiographic side-by-side reads in clinical settings by enhancing the visual perception of changes in tumor size, mass effect, and infiltration across multiple slices over time. In this approach, co-registered, consecutive MRI scans are displayed in a slide deck, where one slide displays multiple brain slices of a single timepoint in an array (e.g. 3x5 "mosaic" view of slices). The flipbooks are viewed similar to an animated flipbook of cartoons/photos so that subtle radiographic changes are visualized via perceived motion when scrolling through the slides. Importantly, flipbooks can be created easily with free, open-source software. This article describes the step-by-step methodology for creating flipbooks and discusses clinical scenarios for which flipbooks are particularly useful. Example flipbooks are provided in the Online Supplemental Material.

Evaluating Cellularity Estimation Methods: Comparing AI Counting with Pathologists’ Visual Estimates

The development of next-generation sequencing (NGS) has enabled the discovery of cancer-specific driver gene alternations, making precision medicine possible. However, accurate genetic testing requires a sufficient amount of tumor cells in the specimen. The evaluation of tumor content ratio (TCR) from hematoxylin and eosin (H&E)-stained images has been found to vary between pathologists, making it an important challenge to obtain an accurate TCR. In this study, three pathologists exhaustively labeled all cells in 41 regions from 41 lung cancer cases as either tumor, non-tumor or indistinguishable, thus establishing a “gold standard” TCR. We then compared the accuracy of the TCR estimated by 13 pathologists based on visual assessment and the TCR calculated by an AI model that we have developed. It is a compact and fast model that follows a fully convolutional neural network architecture and produces cell detection maps which can be efficiently post-processed to obtain tumor and non-tumor cell counts from which TCR is calculated. Its raw cell detection accuracy is 92% while its classification accuracy is 84%. The results show that the error between the gold standard TCR and the AI calculation was significantly smaller than that between the gold standard TCR and the pathologist’s visual assessment (p<0.05). Additionally, the robustness of AI models across institutions is a key issue and we demonstrate that the variation in AI was smaller than that in the average of pathologists when evaluated by institution. These findings suggest that the accuracy of tumor cellularity assessments in clinical workflows is significantly improved by the introduction of robust AI models, leading to more efficient genetic testing and ultimately to better patient outcomes.

Neonatal jaundice detection in low-resource Mexican settings: possibilities and barriers for innovation with mobile health

BackgroundNeonatal jaundice is a common condition that can lead to brain damage and disabilities when severe cases go undetected. Low- and middle-income countries often lack accurate methods for detecting neonatal jaundice and rely on visual assessment, resulting in a higher incidence of adverse consequences. Picterus Jaundice Pro (Picterus JP), an easy-to-use and affordable smartphone-based screening device for the condition, has demonstrated higher accuracy than visual assessment in Norwegian, Philippine and Mexican newborns. This study aimed to identify the barriers and facilitators to implementing Picterus JP in public health services in low-income settings in Mexico by exploring the current process of neonatal jaundice detection and stakeholders’ perspectives in that context.MethodsQualitative data collection techniques, including one focus group, 15 semi-structured interviews and four observations, were employed in urban and rural health facilities in Oaxaca, Mexico. The participants included medical doctors, nurses and health administrators. The data were analysed by thematic analysis guided by the Consolidated Framework for Implementation Research.ResultsThe analysis yielded four main themes: (I) the current state of neonatal care and NNJ detection, (II) the needs and desires for enhancing NNJ detection, (III) the barriers and facilitators to implementing Picterus JP in the health system and (IV) HCWs’ expectations of Picterus JP. The findings identify deficiencies in the current neonatal jaundice detection process and the participants’ desire for a more accurate method. Picterus JP was perceived as easy to use, useful and compatible with the work routine, but barriers to adoption were identified, including internet deficiencies and costs.ConclusionsThe introduction of Picterus JP as a supporting tool to screen for neonatal jaundice is promising but contextual barriers in the setting must be addressed for successful implementation. There is also an opportunity to optimise visual assessment to improve detection of neonatal jaundice.

Integrating deep learning with non-destructive thermal imaging for precision guava ripeness determination.

To mitigate post-harvest losses and inform harvesting decisions at the same time as ensuring fruit quality, precise ripeness determination is essential. The complexity arises in assessing guava ripeness as a result of subtle alterations in some varieties during the ripening process, making visual assessment less reliable. The present study proposes a non-destructive method employing thermal imaging for guava ripeness assessment, involving obtaining thermal images of guava samples at different ripeness stages, followed by data pre-processing. Five deep learning models (AlexNet, Inception-v3, GoogLeNet, ResNet-50 and VGGNet-16) were applied, and their performances were systematically evaluated and compared. VGGNet-16 demonstrated outstanding performance, achieving average precision of 0.92, average sensitivity of 0.93, average specificity of 0.96, average F1-score of 0.92 and accuracy of 0.92 within a training duration of 484 s. The present study presents a scalable and non-destructive approach for guava ripeness determination, contributing to waste reduction and enhancing efficiency in supply chains and fruit production. These initiatives align with environmentally friendly practices in agriculture. © 2024 Society of Chemical Industry.

Transcutaneous bilirubinometry for detecting jaundice in term or late preterm neonates.

The American Academy of Pediatrics and the Canadian Paediatric Society both advise that all newborns should undergo bilirubin screening before leaving the hospital, and this has become the standard practice in both countries. However, the US Preventive Task Force has found no strong evidence to suggest that this practice of universal screening for bilirubin reduces the occurrence of significant outcomes such as bilirubin-induced neurologic dysfunction or kernicterus. To evaluate the effectiveness of transcutaneous screening compared to visual inspection for hyperbilirubinemia to prevent the readmission of newborns (infants greater than 35 weeks' gestation) for phototherapy. We searched CENTRAL, MEDLINE, Embase, CINAHL, ClinicalTrials.gov, ICTRP, and ISRCTN in June 2023. We also searched conference proceedings, and the reference lists of included studies. We included randomized controlled trials (RCTs), quasi-randomized, cluster-randomized, or prospective cohort studies with control arm that evaluated the use of transcutaneous bilirubin (TcB) screening for hyperbilirubinemia in newborns before hospital discharge. We used standard methodologic procedures expected by Cochrane. We evaluated treatment effects using a fixed-effect model with risk ratio (RR) and 95% confidence intervals (CI) for categorical data and mean, standard deviation (SD), and mean difference (MD) for continuous data. We used the GRADE approach to evaluate the certainty of evidence. We identified one RCT (1858 participants) that met our inclusion criteria. The study included 1858 African newborns at 35 weeks' gestation or greater who were receiving routine care at a well-baby nursery, and were randomly recruited prior to discharge to undergo TcB screening. The study had good methodologic quality. TcB screening versus visual assessment of hyperbilirubinemia in newborns: - may reduce readmission to the hospital for hyperbilirubinemia (RR 0.25, 95% CI 0.14 to 0.46; P < 0.0001; moderate-certainty evidence); - probably has little or no effect on the rate of exchange transfusion (RR 0.20, 95% CI 0.01 to 14.16; low-certainty evidence); - may increase the number of newborns who require phototherapy prior to discharge (RR 2.67, 95% CI 1.56 to 4.55; moderate-certainty evidence). - probably has little or no effect on the rate of acute bilirubin encephalopathy (RR 0.33, 95% CI 0.01 to 8.18; low-certainty evidence). The study did not evaluate or report cost of care. Moderate-certainty evidence suggests that TcB screening may reduce readmission for hyperbilirubinemia compared to visual inspection. Low-certainty evidence also suggests that TcB screening probably has little or no effect on the rate of exchange transfusion compared to visual inspection. However, moderate-certainty evidence suggests that TcB screening may increase the number of newborns that require phototherapy before discharge compared to visual inspection. Low-certainty evidence suggests that TcB screening probably has little or no effect on the rate of acute bilirubin encephalopathy compared to visual inspection. Given that we have only identified one RCT, further studies are necessary to determine whether TcB screening can help to reduce readmission and complications related to neonatal hyperbilirubinemia. In settings with limited newborn follow-up after hospital discharge, identifying newborns at risk of severe hyperbilirubinemia before hospital discharge will be important to plan targeted follow-up of these infants.

Assessment of the efficiency of dental excavation methods using laser speckle imaging.

This paper introduces a novel application of the laser speckle technique in dentistry, focusing on assessing the efficiency of dental excavation methods used to remove decayed tooth structure. The aim is to evaluate the efficiency of two chemo-mechanical agents and the high-speed drill using the laser speckle technique, which offers objective, non-invasive, and real-time evaluation capabilities. Extracted human primary molars with active occlusal carious lesions were sectioned into three parts, with each part allocated to one of three groups: Group 1 (Brix3000®), Group 2 (Papacarie DUO®), and Group 3 (High-speed drill mechanical caries removal). Caries removal was performed using the designated agent or method for each group. After caries excavation, speckle imaging using a 632.8 nm laser was conducted. Additionally, SEM was used to acquire micro-photographs of the surface morphology of the treated samples. The findings reveal insights into the comparative efficiency of the three dental excavation agents and methods using the laser speckle technique. The speckle parameters extracted from speckle patterns generated by treated teeth provide valuable information for evaluating the performance of the excavation methods. The scanning electron microscopy images also offer detailed visual evidence to support the analysis. This paper demonstrates the potential of the laser speckle technique for assessing the efficiency of dental excavation methods. The objective, non-invasive, and real-time evaluation provided offers advantages over subjective visual assessment and manual measurements.

Arrhythmic risk stratification in non-ischemic cardiomyopathy using cardiovascular magnetic resonance

Abstract Background Myocardial fibrosis (MF) is an essential component of the arrhythmic substrate for ventricular arrhythmias (VAs). Aims To determine whether total myocardial fibrosis (TF) and gray zone fibrosis (GZF), assessed using cardiovascular magnetic resonance, are better than LVEF in predicting ventricular arrhythmias in patients with non-ischemic cardiomyopathy (NICM). Methods and results Patients with NICM in a derivation cohort (n=866) and a validation cohort (n=848) underwent quantification of TF and GZF. The primary composite endpoint was sudden cardiac death (SCD) or VAs (ventricular fibrillation or sustained ventricular tachycardia). The primary endpoint was met by 52/866 (6.0%) patients in the derivation cohort (follow-up of 7.5 years [interquartile range, IQR] 5.2-9.3). In competing risk analyses, MF on visual assessment (MFVA) predicted the primary endpoint (hazard ratio [HR] 5.83, 95% confidence intervals [CI] 3.15-10.8). Quantified MF measures permitted categorization into 3 risk groups: a TF &amp;gt;0 ≤ 10 g was associated with an intermediate risk (HR: 4.03 [95% CI 1.99-8.16]) and a TF &amp;gt; 10g with the highest risk (HR: 9.17 [95% CI 4.64-18.1]) compared to no MFVA (lowest risk) (Figure). Similar trends were observed in the validation cohort. Categorization into 3 risk groups was achievable using TF or GZF in combination or in isolation. In contrast, LVEF&amp;lt;35% was a poor predictor of the primary endpoint (validation cohort HR:1.99, 95% CI 0.99-0.41). Conclusions MFVA is a strong predictor of SCD and VAs in NICM. TF and GZF mass added incremental value to MFVA. In contrast, LVEF was not useful in arrhythmic risk stratification.

Visual training after central retinal loss limits structural white matter degradation: an MRI study

BackgroundMacular degeneration of the eye is a common cause of blindness and affects 8% of the worldwide human population. In adult cats with bilateral lesions of the central retina, we explored the possibility that motion perception training can limit the associated degradation of the visual system. We evaluated how visual training affects behavioral performance and white matter structure. Recently, we proposed (Kozak et al. in Transl Vis Sci Technol 10:9, 2021) a new motion-acuity test for low vision patients, enabling full visual field functional assessment through simultaneous perception of shape and motion. Here, we integrated this test as the last step of a 10-week motion-perception training.ResultsCats were divided into three groups: retinal-lesioned only and two trained groups, retinal-lesioned trained and control trained. The behavioral data revealed that trained cats with retinal lesions were superior in motion tasks, even when the difficulty relied only on acuity. 7 T-MRI scanning was done before and after lesioning at 5 different timepoints, followed by Fixel-Based and Fractional Anisotropy Analysis. In cats with retinal lesions, training resulted in a more localized and reduced percentage decrease in Fixel-Based Analysis metrics in the dLGN, caudate nucleus and hippocampus compared to untrained cats. In motion-sensitive area V5/PMLS, the significant decreases in fiber density were equally strong in retinal-lesioned untrained and trained cats, up to 40% in both groups. The only cortical area with Fractional Anisotropy values not affected by central retinal loss was area V5/PMLS. In other visual ROIs, the Fractional Anisotropy values increased over time in the untrained retinal lesioned group, whereas they decreased in the retinal lesioned trained group and remained at a similar level as in trained controls.ConclusionsOverall, our MRI results showed a stabilizing effect of motion training applied soon after central retinal loss induction on white matter structure. We propose that introducing early motion-acuity training for low vision patients, aimed at the intact and active retinal peripheries, may facilitate brain plasticity processes toward better vision.

From Pixels to Prognosis: Unlocking the Potential of Deep Learning in Fibrotic Lung Disease Imaging Analysis.

The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis IPF has created an urgent need for reliable biomarkers to predict disease progression and treatment response. Some patients experience stable disease trajectories, while others deteriorate rapidly, making treatment decisions challenging. High-resolution chest CT has become crucial for diagnosis, but visual assessments by radiologists suffer from low reproducibility and high interobserver variability. To address these issues, computer-based image analysis, called quantitative CT, has emerged. However, many quantitative CT methods rely on human input for training, therefore potentially incorporating human error into computer training. Rapid advances in artificial intelligence, specifically deep learning, aim to overcome this limitation by enabling autonomous quantitative analysis. While promising, deep learning also presents challenges including the need to minimize algorithm biases, ensuring explainability, and addressing accessibility and ethical concerns. This review explores the development and application of deep learning in improving the imaging process for fibrotic lung disease.

Method for selecting viewpoints of architectural heritage landscapes

The selection of viewpoints is a crucial aspect in conducting visual impact assessments of architectural heritage. To address this issue, a quantitative viewpoint selection method based on GIS spatial analysis is proposed. The first step involves defining the factors that influence the selection of viewpoints for architectural heritage. Next, the city’s historical architectural areas are divided into units based on courtyards. Subsequently, a candidate set of viewpoints is constructed by extracting the skeleton lines and characteristic points of sub-regions. Finally, following the principles of viewpoint selection, the method achieves the selection of viewpoints for the visual impact assessment of architectural heritage. The Forbidden City is chosen as the practical area for implementation, and the results demonstrate that the viewshed coverage range in the architectural heritage area is as high as 96%. It represents that this approach for visual impact assessments of architectural heritage is more grounded.

Experimental Open Air Burning of Vegetation Enhances Organic Matter Chemical Heterogeneity Compared to Laboratory Burns.

Wildfires produce solid residuals that have unique chemical and physical properties compared to unburned materials, which influence their cycling and fate in the natural environment. Visual burn severity assessment is used to evaluate post-fire alterations to the landscape in field-based studies, yet muffle furnace methods are commonly used in laboratory studies to assess molecular scale alterations along a temperature continuum. Here, we examined solid and leachable organic matter characteristics from chars visually characterized as low burn severity that were created either on an open air burn table or from low-temperature muffle furnace burns. We assessed how the different combustion conditions influence solid and dissolved organic matter chemistries and explored the potential influence of these results on the environmental fate and reactivity. Notably, muffle furnace chars produced less leachable carbon and nitrogen than open air chars across land cover types. Organic matter produced from muffle furnace burns was more homogeneous than open air chars. This work highlights chemical heterogeneities that exist within a single burn severity category, potentially influencing our conceptual understanding of pyrogenic organic matter cycling in the natural environment, including transport and processing in watersheds. Therefore, we suggest that open air burn studies are needed to further advance our understanding of pyrogenic organic matter's environmental reactivity and fate.

Quantitative analysis of blooming artifact caused by calcification based on X-ray energy difference using computed tomography

Blooming artifacts caused by calcifications appearing on computed tomography (CT) images lead to an underestimation of the coronary artery lumen size, and higher X-ray energy levels are suggested to reduce the blooming artifacts with subjective visual assessment. This study aimed to evaluate the effect of higher X-ray energy levels on the quantitative measurement of adjacent pixels affected by calcification using CT images. In this two-part study, CT images were acquired from dual-energy CT scanners by changing the X-ray energy levels such as kilovoltage peak (kVp) and kilo-electron volts (keV). Adjacent pixels affected by calcification were measured using the brightened length, excluding the actual calcified length, as determined by the full width at third maximum. In a separate clinical study, the adjacent affected pixels associated with 23 calcifications across 10 patients were measured using the same method as that used in the phantom study. Phantom and clinical studies showed that the change in kVp (field of view [FOV] 300 mm: p = 0.167, 0.494, and 0.861 for vendors 1, 2, and 3, respectively) and keV levels (p = 0.178 for vendor 2) failed to reduce the adjacent pixels affected by calcification, respectively. Moreover, the change in keV levels showed different aspects of adjacent pixels affected by calcification in the phantom study (FOV 300 mm: no significant difference [p = 0.191], increase [p < 0.001], and decrease [p < 0.001] for vendors 1, 2, and 3, respectively). Quantitative measurements revealed no significant relationship between higher X-ray energy levels and the adjacent pixels affected by calcification.

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography

Background. Breast background parenchymal enhancement (BPE) is correlated with the risk of breast cancer. BPE level is currently assessed by radiologists in contrast-enhanced mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described in breast imaging reporting and data system (BI-RADS). However, BPE classification remains subject to intra- and inter-reader variability. Fully automated methods to assess BPE level have already been developed in breast contrast-enhanced MRI (CE-MRI) and have been shown to provide accurate and repeatable BPE level classification. However, to our knowledge, no BPE level classification tool is available in the literature for CEM. Materials and methods. A BPE level classification tool based on deep learning has been trained and optimized on 7012 CEM image pairs (low-energy and recombined images) and evaluated on a dataset of 1013 image pairs. The impact of image resolution, backbone architecture and loss function were analyzed, as well as the influence of lesion presence and type on BPE assessment. The evaluation of the model performance was conducted using different metrics including 4-class balanced accuracy and mean absolute error. The results of the optimized model for a binary classification: minimal/mild versus moderate/marked, were also investigated. Results. The optimized model achieved a 4-class balanced accuracy of 71.5% (95% CI: 71.2–71.9) with 98.8% of classification errors between adjacent classes. For binary classification, the accuracy reached 93.0%. A slight decrease in model accuracy is observed in the presence of lesions, but it is not statistically significant, suggesting that our model is robust to the presence of lesions in the image for a classification task. Visual assessment also confirms that the model is more affected by non-mass enhancements than by mass-like enhancements. Conclusion. The proposed BPE classification tool for CEM achieves similar results than what is published in the literature for CE-MRI.

Rapid survey for assessing effective cataract surgical coverage (eCSC) and effective refractive error coverage (eREC)-Novel indicators of universal eye health.

As per the recent World Health Organization estimates, approximately 2.2 billion people have near and distance vision impairment (VI) globally, and out of this almost 50% is avoidable. The Rapid Assessment of Avoidable Visual Impairment survey was a cross-sectional study conducted in September 2021, using cluster random sampling in 42 clusters with a cluster size of 140, giving a total of 6000 participants. Two teams comprising of trained optometrists and social workers conducted the ocular examination which included unaided, pinhole, and aided visual acuity assessments followed by examination of the anterior segment and lens. Distance visual acuity was measured using simplified tumbling "E" charts of different sizes for VA of 6/12, 6/18, and 6/60. The lens assessment was done in an un-dilated pupil with torch light by the optometrist. Overall, 6520 individuals aged 6 years and above were enumerated, of whom 5440 (83.4%) were examined. The response rate for examination was better among females (93.1%) than males (73.9%), and it decreased from 93.8% in the age group 6-15 years to 77.1% in the 45+ age group. The prevalence of blindness and VI were 0.18% (95% CI: 0.06-0.29) and 4.19% (95%CI: 3.65-4.72), respectively. The major causes of VI in all age groups were uncorrected refractive error (65.4%), cataract (23.7%), cataract surgical complications (2.6%), corneal opacity (0.4%), and other posterior segment diseases (7.5%). The effective cataract surgical coverage (eCSC) was 61.8%, effective refractive error coverage (eREC) for distance vision was 59.8%, and eREC for near vision was 47.0%. The RAAVI methodology is suitable to measure effective coverage in the general population, both for baseline measurement and periodic monitoring. The 2030 targets for the surveyed district are 90% eCSC and 100% eREC. Such exercises need to be conducted in each district of the country to determine the baseline and target values of effective coverage.

Intraoperative left-sided colorectal anastomotic testing in clinical practice: a multi-treatment machine-learning analysis of the iCral3 prospective cohort.

Current evidence about intraoperative anastomotic testing after left-sided colorectal resections is still controversial. The aim of this study was to analyze the impact of Indocyanine Green fluorescent angiography (ICG-FA) and air-leak test (ALT) over standard assessment on anastomotic leakage (AL) rates according to surgeon's perception of anastomosis perfusion and/or integrity in clinical practice. A database of 2061 patients who underwent left-sided colorectal resections was selected from patients enrolled in a prospective multicenter study. It was retrospectively analyzed through a multi-treatment machine-learning model considering standard visual assessment (NW; No. = 899; 43.6%) as the reference treatment arm, compared to ICG-FA alone (WP; No. = 409; 19.8%), ALT alone (WI; No. = 420; 20.4%) or both (WPI; No. = 333; 16.2%). Twenty-four covariates potentially affecting the outcomes were included and balanced into the model within the subgroups. The primary endpoint was AL, the secondary endpoints were overall morbidity (OM), major morbidity (MM), reoperation for AL, and mortality. All the results were reported as odds ratio (OR) with 95% confidence intervals (95%CI). The WPI subgroup showed significantly higher AL risk (OR 1.91; 95% CI 1.02-3.59; p 0.043), MM risk (OR 2.35; 95% CI 1.39-3.97; p 0.001), and reoperation for AL risk (OR 2.44; 95% CI 1.12-5.31; p 0.025). No other significant differences were recorded. This study showed that the surgeons' perception of both anastomotic perfusion and integrity (WPI subgroup) was associated to a significantly higher risk of AL and related morbidity, notwithstanding the extensive use of both ICG-FA and ALT testing.

Finding Perforator "Freeway" for Design Optimization of Expanded Flaps by Indocyanine Green Angiography.

Indocyanine green angiography (ICGA) is a useful tool for the visual assessment of superficial blood flow. Herein, we used ICGA to visualize perforator branches and linking vessels to provide a road map for flap design of an expanded flap. Twenty-eight expansions were planned to use back-cut technique in 26 patients. ICGA was used to visualize perforator branching pattern with the linking vessels and the venous network in the expanded flap before expander explantation. The appropriate perforator was selected, and the flap was designed following the axiality of its branch linked by true anastomoses. The vein running closely was chosen as the axial vein. The back cut was designed to avoid transection of the axial artery and vein. Patient demographics, defect characteristics, and reconstructive outcomes were assessed. ICGA clearly visualized the perforator branches and the linking vessels in the expanded flap at the head and neck, trunk, and extremity. The back-cut flap containing the axial artery and vein was raised successfully in 27 expansions. The arterial perforator and superficial vein separated greatly and resulted in design modification from back-cut to advancement flap in one expansion. All expanded flaps met the reconstructive needs and exhibited complete survival. ICGA allowed the visualization of the preoperative topography of the vascular network in the expanded flap and helped surgeons locate the vascular axis and perform an appropriate back-cut design for efficient and safe flap transfer.

Validity and norms for young adults for the Aggie Figures Learning Test.

The purpose of the present study was to demonstrate the convergent validity of the AFLT and to provide norms for an adult population ranging from 18 to 58. 140 healthy volunteers participated in the study. They ranged in age from 18 to 58 years, with 12 to 25 years of education. Subjects were assessed with a comprehensive neuropsychological battery which included the ALFT (A version) and the Rey-Osterrieth Complex Figure (ROCF). Positive correlations were found between the scores of both tests for recent memory (r = 0.606, p < 0.01), delayed free recall (r = 0.534, p < 0.01) and recognition memory (r = 0.202, p < 0.05). These results demonstrate the convergent validity of the AFLT. The AFLT is a visual memory assessment tool with adequate psychometric properties, which allows a comprehensive evaluation of visual memory processes.

Automated Scoring of Alzheimer’s Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation

Background: Application of visual scoring scales for regional atrophy in Alzheimer’s disease (AD) in clinical settings is limited by their high time cost and low intra/inter-rater agreement. Objective: To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI). Methods: We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impairment [MCI], and 633 with AD). The occupancy indices for each regional volume were calculated by dividing each volume by the size of the lateral and inferior ventricular volumes. MR images from 355 participants (119 CN, 119 MCI, and 117 AD) from three different centers were used for validation. Two neuroradiologists performed visual assessments of the medial temporal, posterior, and global cortical atrophy scores in the frontal lobe using T1-weighted MR images. Images were also analyzed using the deep learning-based segmentation software, Neurophet AQUA. Cutoff values for the three scores were determined using the data distribution according to age. The scoring results were compared for consistency and reliability. Results: Four volumetric-driven scoring results showed a high correlation with the visual scoring results for AD, MCI, and CN. The overall agreement with human raters was weak-to-moderate for atrophy scoring in CN participants, and good-to-almost perfect in AD and MCI participants. AD subtyping by automated scores also showed usefulness as a research tool. Conclusions: Determining AD subtypes using automated atrophy scoring for late-MCI and AD could be useful in clinical settings or multicenter studies with large datasets.

Potential non-invasive detection of lesions in broiler femur heads: application of the DXA imaging system

Leg health is a significant economic and welfare concern for the poultry industry. Current methods of detection rely on visual assessment of the legs and gait scores and bone scoring during necropsy for full characterization. Additionally, the current scoring of femurs only examines the external surface of the femoral head. Through the use of the dual-energy X-ray absorptiometry (DXA) imaging system, we show the presence of a necrotic region in the femurs that would otherwise be considered healthy based on the current evaluation procedures. Importantly, these lesions were present in almost 60% (22 of 37) of femurs that scored normal for femoral head necrosis (FHN). Additionally, these femurs showed greater bone mineral content (BMC) relative to weight compared to their counterparts with no lucent lesions (6.95% ± 0.20% vs. 6.26% ± 0.25; p = 0.038). Identification of these lesions presents both a challenge and an opportunity. These subclinical lesions are likely to be missed in routine scoring procedures for FHN and can inadvertently impact the characterization of the disease and genetic selection programs. Furthermore, this imaging system can be used for in vivo, ex vivo, and embryonic (egg) studies and, therefore, constitutes a potential non-invasive method for early detection of bone lesions in chickens and other avian species.