Novel AI-Based Algorithm for the Automated Measurement of Cervical Sagittal Balance Parameters. A Validation Study on Pre- and Postoperative Radiographs of 129 Patients

STUDY DESIGN

Retrospective, mono-centric cohort research study.

OBJECTIVES

The analysis of cervical sagittal balance parameters is essential for preoperative planning and dependent on the physician's experience. A fully automated artificial intelligence-based algorithm could contribute to an objective analysis and save time. Therefore, this algorithm should be validated in this study.

METHODS

Two surgeons measured C2-C7 lordosis, C1-C7 Sagittal Vertical Axis (SVA), C2-C7-SVA, C7-slope and T1-slope in pre- and postoperative lateral cervical X-rays of 129 patients undergoing anterior cervical surgery. All parameters were measured twice by surgeons and compared to the measurements by the AI algorithm consisting of 4 deep convolutional neural networks. Agreement between raters was quantified, among other metrics, by mean errors and single measure intraclass correlation coefficients for absolute agreement.

RESULTS

ICC-values for intra- (range: .92-1.0) and inter-rater (.91-1.0) reliability reflect excellent agreement between human raters. The AI-algorithm could determine all parameters with excellent ICC-values (preop:0.80-1.0; postop:0.86-.99). For a comparison between the AI algorithm and 1 surgeon, mean errors were smallest for C1-C7 SVA (preop: -.3 mm (95% CI:-.6 to -.1 mm), post: .3 mm (.0-.7 mm)) and largest for C2-C7 lordosis (preop:-2.2° (-2.9 to -1.6°), postop: 2.3°(-3.0 to -1.7°)). The automatic measurement was possible in 99% and 98% of pre- and postoperative images for all parameters except T1 slope, which had a detection rate of 48% and 51% in pre- and postoperative images.

CONCLUSION

This study validates that an AI-algorithm can reliably measure cervical sagittal balance parameters automatically in patients suffering from degenerative spinal diseases. It may simplify manual measurements and autonomously analyze large-scale datasets. Further studies are required to validate the algorithm on a larger and more diverse patient cohort.

Speech changes in old age: Methodological considerations for speech-based discrimination of healthy ageing and Alzheimer's disease

BACKGROUND

Recent evidence suggests that speech substantially changes in ageing. As a complex neurophysiological process, it can accurately reflect changes in the motor and cognitive systems underpinning human speech. Since healthy ageing is not always easily discriminable from early stages of dementia based on cognitive and behavioural hallmarks, speech is explored as a preclinical biomarker of pathological itineraries in old age. A greater and more specific impairment of neuromuscular activation, as well as  a specific cognitive and linguistic impairment in dementia, unchain discriminating changes in speech. Yet, there is no consensus on such discriminatory speech parameters, neither on how they should be elicited and assessed.

AIMS

To provide a state-of-the-art on speech parameters that allow for early discrimination between healthy and pathological ageing; the aetiology of these parameters; the effect of the type of experimental stimuli on speech elicitation and the predictive power of different speech parameters; and the most promising methods for speech analysis and their clinical implications.

METHODS & PROCEDURES

A scoping review methodology is used in accordance with the PRISMA model. Following a systematic search of PubMed, PsycINFO and CINAHL, 24 studies are included and analysed in the review.

MAIN CONTRIBUTION

The results of this review yield three key questions for the clinical assessment of speech in ageing. First, acoustic and temporal parameters are more sensitive to changes in pathological ageing and, of these two, temporal variables are more affected by cognitive impairment. Second, different types of stimuli can trigger speech parameters with different degree of accuracy for the discrimination of clinical groups. Tasks with higher cognitive load are more precise in eliciting higher levels of accuracy. Finally, automatic speech analysis for the discrimination of healthy and pathological ageing should be improved for both research and clinical practice.

CONCLUSIONS & IMPLICATIONS

Speech analysis is a promising non-invasive tool for the preclinical screening of healthy and pathological ageing. The main current challenges of speech analysis in ageing are the automatization of its clinical assessment and the consideration of the speaker's cognitive background during evaluation.

WHAT THIS PAPER ADDS

What is already known on the subject Societal aging  goes hand in hand with the rising incidence of ageing-related neurodegenerations, mainly Alzheimer's disease (AD). This is particularly noteworthy in countries with longer life expectancies. Healthy ageing and early stages of AD share a set of cognitive and behavioural characteristics. Since there is no cure for dementias, developing methods for accurate discrimination of healthy ageing and early AD is currently a priority. Speech has been described as one of the most significantly impaired features in AD. Neuropathological alterations in motor and cognitive systems would underlie specific speech impairment in dementia. Since speech can be evaluated quickly, non-invasively and inexpensively, its value for the clinical assessment of ageing itineraries may be particularly high. What this paper adds to existing knowledge Theoretical and experimental advances in the assessment of speech as a marker of AD have developed rapidly over the last decade. Yet, they are not always known to clinicians. Furthermore, there is a need to provide an updated state-of-the-art on which speech features are discriminatory to AD, how they can be assessed, what kind of results they can yield, and how such results should be interpreted. This article provides an updated overview of speech profiling, methods of speech measurement and analysis, and the clinical power of speech assessment for early discrimination of AD as the most common cause of dementia. What are the potential or actual clinical implications of this work? This article provides an overview of the predictive potential of different speech parameters in relation to AD cognitive impairment. In addition, it discusses the effect that the cognitive state, the type of elicitation task and the type of assessment method may have on the results of the speech-based analysis in ageing.

Ultrasensitive Automatic Detection of Small Molecules by Membrane Imaging of Single Molecule Assays

Determination of trace amounts of targets or even a single molecule target has always been a challenge in the detection field. Digital measurement methods established for single molecule counting of proteins, such as single molecule arrays (Simoa) or dropcast single molecule assays (dSimoa), are not suitable for detecting small molecule, because of the limited category of small molecule antibodies and the weak signal that can be captured. To address this issue, we have developed a strategy for single molecule detection of small molecules, called small molecule detection with single molecule assays (smSimoa). In this strategy, an aptamer is used as a recognition element, and an addressable DNA Nanoflower (DNF) attached on the magnetic beads surface, which exhibit fluorescence imaging, is employed as the output signal. Accompanied by digital imaging and automated counting analysis, E2 at the attomolar level can be measured. The smSimoa breaks the barrier of small molecule detection concentration and provides a basis for high throughput detection of multiple substances with fluorescence encoded magnetic beads.

Capturing Eating Behavior from Video Analysis: A Systematic Review

Current methods to detect eating behavior events (i.e., bites, chews, and swallows) lack objective measurements, standard procedures, and automation. The video recordings of eating episodes provide a non-invasive and scalable source for automation. Here, we reviewed the current methods to automatically detect eating behavior events from video recordings. According to PRISMA guidelines, publications from 2010-2021 in PubMed, Scopus, ScienceDirect, and Google Scholar were screened through title and abstract, leading to the identification of 277 publications. We screened the full text of 52 publications and included 13 for analysis. We classified the methods in five distinct categories based on their similarities and analyzed their accuracy. Facial landmarks can count bites, chews, and food liking automatically (accuracy: 90%, 60%, 25%). Deep neural networks can detect bites and gesture intake (accuracy: 91%, 86%). The active appearance model can detect chewing (accuracy: 93%), and optical flow can count chews (accuracy: 88%). Video fluoroscopy can track swallows but is currently not suitable beyond clinical settings. The optimal method for automated counts of bites and chews is facial landmarks, although further improvements are required. Future methods should accurately predict bites, chews, and swallows using inexpensive hardware and limited computational capacity. Automatic eating behavior analysis will allow the study of eating behavior and real-time interventions to promote healthy eating behaviors.

Automatic measurements of fetal intracranial volume from 3D ultrasound scans

Three-dimensional fetal ultrasound is commonly used to study the volumetric development of brain structures. To date, only a limited number of automatic procedures for delineating the intracranial volume exist. Hence, intracranial volume measurements from three-dimensional ultrasound images are predominantly performed manually. Here, we present and validate an automated tool to extract the intracranial volume from three-dimensional fetal ultrasound scans. The procedure is based on the registration of a brain model to a subject brain. The intracranial volume of the subject is measured by applying the inverse of the final transformation to an intracranial mask of the brain model. The automatic measurements showed a high correlation with manual delineation of the same subjects at two gestational ages, namely, around 20 and 30 weeks (linear fitting R2(20 weeks) = 0.88, R2(30 weeks) = 0.77; Intraclass Correlation Coefficients: 20 weeks=0.94, 30 weeks = 0.84). Overall, the automatic intracranial volumes were larger than the manually delineated ones (84 ± 16 vs. 76 ± 15 cm3; and 274 ± 35 vs. 237 ± 28 cm3), probably due to differences in cerebellum delineation. Notably, the automated measurements reproduced both the non-linear pattern of fetal brain growth and the increased inter-subject variability for older fetuses. By contrast, there was some disagreement between the manual and automatic delineation concerning the size of sexual dimorphism differences. The method presented here provides a relatively efficient way to delineate volumes of fetal brain structures like the intracranial volume automatically. It can be used as a research tool to investigate these structures in large cohorts, which will ultimately aid in understanding fetal structural human brain development.

Automated Artificial Intelligence-Based Assessment of Lower Limb Alignment Validated on Weight-Bearing Pre- and Postoperative Full-Leg Radiographs

The assessment of the knee alignment using standing weight-bearing full-leg radiographs (FLR) is a standardized method. Determining the load-bearing axis of the leg requires time-consuming manual measurements. The aim of this study is to develop and validate a novel algorithm based on artificial intelligence (AI) for the automated assessment of lower limb alignment. In the first stage, a customized mask-RCNN model was trained to automatically detect and segment anatomical structures and implants in FLR. In the second stage, four region-specific neural network models (adaptations of UNet) were trained to automatically place anatomical landmarks. In the final stage, this information was used to automatically determine five key lower limb alignment angles. For the validation dataset, weight-bearing, antero-posterior FLR were captured preoperatively and 3 months postoperatively. Preoperative images were measured by the operating orthopedic surgeon and an independent physician. Postoperative images were measured by the second rater only. The final validation dataset consisted of 95 preoperative and 105 postoperative FLR. The detection rate for the different angles ranged between 92.4% and 98.9%. Human vs. human inter-(ICCs: 0.85−0.99) and intra-rater (ICCs: 0.95−1.0) reliability analysis achieved significant agreement. The ICC-values of human vs. AI inter-rater reliability analysis ranged between 0.8 and 1.0 preoperatively and between 0.83 and 0.99 postoperatively (all p < 0.001). An independent and external validation of the proposed algorithm on pre- and postoperative FLR, with excellent reliability for human measurements, could be demonstrated. Hence, the algorithm might allow for the objective and time saving analysis of large datasets and support physicians in daily routine.

Towards an automatic narcolepsy detection on ambiguous sleep staging and sleep transition dynamics joint model

Objective.Mixing/dissociation of sleep stages in narcolepsy adds to the difficulty in automatic sleep staging. Moreover, automatic analytical studies for narcolepsy and multiple sleep latency test (MSLT) have only done automatic sleep staging without leveraging the sleep stage profile for further patient identification. This study aims to establish an automatic narcolepsy detection method for MSLT.Approach.We construct a two-phase model on MSLT recordings, where ambiguous sleep staging and sleep transition dynamics make joint efforts to address this issue. In phase 1, we extract representative features from electroencephalogram (EEG) and electrooculogram (EOG) signals. Then, the features are input to an EasyEnsemble classifier for automatic sleep staging. In phase 2, we investigate sleep transition dynamics, including sleep stage transitions and sleep stages, and output likelihood of narcolepsy by virtue of principal component analysis (PCA) and a logistic regression classifier. To demonstrate the proposed framework in clinical application, we conduct experiments on 24 participants from our hospital, considering ten patients with narcolepsy and fourteen patients with MSLT negative.Main results.Applying the two-phase leave-one-subject-out testing scheme, the model reaches an accuracy, sensitivity, and specificity of 87.5%, 80.0%, and 92.9% for narcolepsy detection. Influenced by disease pathology, accuracy of automatic sleep staging in narcolepsy appears to decrease compared to that in the non-narcoleptic population.Significance.This method can automatically and efficiently distinguish patients with narcolepsy based on MSLT. It probes into the amalgamation of automatic sleep staging and sleep transition dynamics for narcolepsy detection, which would assist clinic and neuroelectrophysiology specialists in visual interpretation and diagnosis.

An Automatic on Top Analysis of Single Scan Tracks to Evaluate the Laser Powder Bed Fusion Building Parameters

The production of dense samples produced by laser powder bed fusion (LPBF) is mainly determined by the choice of the best combination of construction parameters. Parameter optimization is the first step in the definition of an LPBF process for new alloys or systems. With this goal, much research uses the single scan track (SST) approach for a preliminary parameter screening. This study investigates the definition of a computer-aided method by using an automatic on top analysis for the characterization of SSTs, with the aim of finding ranges of laser power and scan speed values for massive production. An innovative algorithm was implemented to discard non-continuous scans and to measure the SSTs quality using three regularity indexes. Only open source software were used to fine tune this approach. The obtained results on Al4Cu and AlSi10Mg realized with two different commercial systems suggest that it is possible to use this method to easily narrow the process parameter window that allows the production of dense samples.

Quantitative Assessment of Ciliary Ultrastructure with the Use of Automatic Analysis: PCD Quant

The ciliary ultrastructure can be damaged in various situations. Such changes include primary defects found in primary ciliary dyskinesia (PCD) and secondary defects developing in secondary ciliary dyskinesia (SCD). PCD is a genetic disease resulting from impaired ciliary motility causing chronic disease of the respiratory tract. SCD is an acquired condition that can be caused, for example, by respiratory infection or exposure to tobacco smoke. The diagnosis of these diseases is a complex process with many diagnostic methods, including the evaluation of ciliary ultrastructure using transmission electron microscopy (the golden standard of examination). Our goal was to create a program capable of automatic quantitative analysis of the ciliary ultrastructure, determining the ratio of primary and secondary defects, as well as analysis of the mutual orientation of cilia in the ciliary border. PCD Quant, a program developed for the automatic quantitative analysis of cilia, cannot yet be used as a stand-alone method for evaluation and provides limited assistance in classifying primary and secondary defect classes and evaluating central pair angle deviations. Nevertheless, we see great potential for the future in automatic analysis of the ciliary ultrastructure.

Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders

STUDY OBJECTIVES

To assess the performance of the single-channel automatic sleep staging (AS) software ASEEGA in adult patients diagnosed with various sleep disorders.

METHODS

Sleep recordings were included of 95 patients (38 women, 40.5 ± 13.7 years) diagnosed with insomnia (n = 23), idiopathic hypersomnia (n = 24), narcolepsy (n = 24), and obstructive sleep apnea (n = 24). Visual staging (VS) was performed by two experts (VS1 and VS2) according to the American Academy of Sleep Medicine rules. AS was based on the analysis of a single electroencephalogram channel (Cz-Pz), without any information from electro-oculography nor electromyography. The epoch-by-epoch agreement (concordance and Conger's coefficient [κ]) was compared pairwise (VS1-VS2, AS-VS1, AS-VS2) and between AS and consensual VS. Sleep parameters were also compared.

RESULTS

The pairwise agreements were: between AS and VS1, 78.6% (κ = 0.70); AS and VS2, 75.0% (0.65); and VS1 and VS2, 79.5% (0.72). Agreement between AS and consensual VS was 85.6% (0.80), with the following distribution: insomnia 85.5% (0.80), narcolepsy 83.8% (0.78), idiopathic hypersomnia 86.1% (0.68), and obstructive sleep disorder 87.2% (0.82). A significant low-amplitude scorer effect was observed for most sleep parameters, not always driven by the same scorer. Hypnograms obtained with AS and VS exhibited very close sleep organization, except for 80% of rapid eye movement sleep onset in the group diagnosed with narcolepsy missed by AS.

CONCLUSIONS

Agreement between AS and VS in sleep disorders is comparable to that reported in healthy individuals and to interexpert agreement in patients. ASEEGA could therefore be considered as a complementary sleep stage scoring tool in clinical practice, after improvement of rapid eye movement sleep onset detection.

A Novel Automatic Morphologic Analysis of Eyelids Based on Deep Learning Methods

Purpose: To propose a deep-learning-based approach to automatically and objectively evaluate morphologic eyelid features using two-dimensional(2D) digital photographs and to assess the agreement between automatic and manual measurements.Materials and Methods: The 2D photographs of 1378 normal Asian participants (2756 eyes) were included for training, validating and testing the cornea and eyelid segmentation network. Margin reflex distance 1 (MRD1) and margin reflex distance 2 (MRD2) of 406 eyes from 203 participants were manually evaluated by 3 ophthalmologists and the photographs of 406 eyes were measured automatically for 8 morphologic eyelid features. The Spearman's correlation coefficient, intra-class correlation coefficient (ICC) and Bland-Altman analyses were used to determine the agreement between manual and automatic MRDs.Results: The dice coefficient was 0.922 and 0.974 for eyelid and cornea segmentation, respectively. A strong correlation was shown between manually and automatically measured MRD1 (r = 0.993, ICC = 0.996) and MRD2 (r = 0.950, ICC = 0.974). Bland-Altman analyses also showed excellent reliability with bias being 0.04 mmbetween automated and manual MRD1 measurements and 0.06 mm for MRD2. Automatically measured 8 features (MRD1, MRD2, palpebral fissure, medial area, lateral area, cornea area, upper and lower eyelid lengths) were found to be increased with age and peaked around the age range of 21 to 30 years.Conclusions: The proposed novel integrative analysis scheme was comparable with human performance. The approach with excellent reliability and reproductivity showed great potential for automated diagnosis and remote monitoring of eyelid-related diseases.

Technical Note: Fully automatic segmental relaxometry (FASTR) for cardiac magnetic resonance T1 mapping

PURPOSE

Cardiac relaxometry techniques, particularly T1 mapping, have recently gained clinical importance in various cardiac pathologies. Myocardial T1 and extracellular volume are usually calculated from manual identification of left ventricular epicardial and endocardial regions. This is a laborious process, particularly for large volume studies. Here we present a fully automated relaxometry framework (FASTR) for segmental analysis of T1 maps (both native and postcontrast) and partition coefficient (λ).

METHODS

Patients (N = 11) were imaged postacute myocardial infarction on a 1.5T clinical scanner. The scan protocol involved CINE-SSFP imaging, native, and post-contrast T1 mapping using the Modified Look-Locker Inversion (MOLLI) recovery sequence. FASTR consisted of automatic myocardial segmentation of spatio-temporally coregistered CINE images as an initial guess, followed by refinement of the contours on the T1 maps to derive segmental T1 and λ. T1 and λ were then compared to those obtained from two trained expert observers.

RESULTS

Robust endocardial and epicardial contours were achieved on T1 maps despite the presence of infarcted tissue. Relative to experts, FASTR resulted in myocardial Dice coefficients (native T1: 0.752 ± 0.041; postcontrast T1: 0.751 ± 0.057) that were comparable to interobserver Dice (native T1: 0.803 ± 0.045; postcontrast T1: 0.799 ± 0.054). There were strong correlations observed for T1 and λ derived from experts and FASTR (native T1: r = 0.83; postcontrast T1: r = 0.87; λ: r = 0.78; P < 0.0001), which were comparable to inter-expert correlation coefficients (native T1: r = 0.90; postcontrast T1: r = 0.93; λ: r = 0.80; P < 0.0001).

CONCLUSIONS

Our fully automated framework, FASTR, can generate accurate myocardial segmentations for native and postcontrast MOLLI T1 analysis without the need for manual intervention. Such a design is appealing for high volume clinical protocols.

Automatic segmentation for synchrotron-based imaging of porous bread dough using deep learning approach

In recent years, major capability improvements at synchrotron beamlines have given researchers the ability to capture more complex structures at a higher resolution within a very short time. This opens up the possibility of studying dynamic processes and observing resulting structural changes over time. However, such studies can create a huge quantity of 3D image data, which presents a major challenge for segmentation and analysis. Here tomography experiments at the Australian synchrotron source are examined, which were used to study bread dough formulations during rising and baking, resulting in over 460 individual 3D datasets. The current pipeline for segmentation and analysis involves semi-automated methods using commercial software that require a large amount of user input. This paper focuses on exploring machine learning methods to automate this process. The main challenge to be faced is in generating adequate training datasets to train the machine learning model. Creating training data by manually segmenting real images is very labour-intensive, so instead methods of automatically creating synthetic training datasets which have the same attributes of the original images have been tested. The generated synthetic images are used to train a U-Net model, which is then used to segment the original bread dough images. The trained U-Net outperformed the previously used segmentation techniques while taking less manual effort. This automated model for data segmentation would alleviate the time-consuming aspects of experimental workflow and would open the door to perform 4D characterization experiments with smaller time steps.

A Novel Automated Calculation of Basal Cistern Effacement Status on Computed Tomographic Imaging in Traumatic Brain Injury

Introduction

To predict patient outcomes in traumatic brain injury (TBI) lesions, various scores have been proposed, which use objective assessments. These scores, however, rely on the observer's ability to determine them. This study presents a comprehensive, reproducible, and more anatomically stratified objective measurement of the degree of basal cistern effacement in brain computed tomographic (CT) scan images.

Methods

Patients with TBI admitted from August 2015 to February 2016 were included. The control group consisted of non-trauma patients, who had normal brain CT scans. The images were analyzed by an automated volumetric compression ratio (CR) defined as the volume ratio between the parenchymal tissue and the cerebrospinal fluid (CSF) in the basal cisterns. This value was compared with the TBI severity recorded at each patient's admission and a consensus score of the basal cisterns' degree of effacement by manual analysis.

Results

Seventy-three TBI patients were admitted. The mean admission Glasow Coma Scale (GCS) score was 9. In the non-TBI control group, 29 patients were enrolled. The average kappa value for the inter-observer agreement was 0.583. The CR had an inverse linear relationship with the severity of the TBI and the degree of effacement of the basal cisterns. The correlation between the CR value in the midbrain and the specialists' consensus determination was statistically significant (p < 0.01). The CR also showed a difference between the TBI and the control groups (p 0.0001).

Conclusions

The automated CR is a useful objective variable to determine the degree of basal cistern effacement. The proposed ratio has a good correlation with the classical basal cistern effacement classification and TBI severity.

Multifaceted modifications for a cell size-based circulating tumor cell scope technique hold the prospect for large-scale application in general populations

Circulating tumor cells (CTCs) indicate the diagnosis and prognosis of cancer patients, together with benefiting individual treatment and anticancer drug development. However, their large-scale application in general population still requires systematically multifaceted modifications for currently proprietary new technologies based on filtration. We primitively utilized a cell size-based platform to evaluate the recovery efficiency of spiked abnormal cell lines and analyzed circulating abnormal cells (CACs). To dissect the subpopulations of CACs, we conducted immunofluorescent (IF) staining with a combination of unique biomarkers of CTCs and circulating endothelial cells (CECs). Furthermore, we improved the CTC screening system by assessing the feasibility of transferring CTCs for automatic IF analysis, together with simulating and optimizing the circumstances for long-term CTC storage and transportation. We detected CACs in 15 HD candidates with CTC characteristics such as abnormally large cytomorphology, high nuclear-cytoplasmic ratio, and positive for panCK or VIM staining. Thereafter, we improved accuracy of the platform by distinguishing CTCs from CECs, which satisfied the elementary requirement for small-scale CTC screening in HD candidates. Finally, large-scale CTC screening in general population was available after multifaceted modifications including automatic analysis by transferring CTCs on slides, choosing the appropriate blood-collecting tube, optimizing the conditions for long-term CTC storage and transportation, and evaluating the potential effect on the CTC phenotype. Hence, we systematically modified the scope of technique parameters, improved the accuracy of early cancer detection, and made it realizable for large-scale CTC or CEC screening in general population.

[Design and implementation of an automatic analysis system for magnetic resonance quality detection based on QT]

The quality inspection of magnetic resonance imaging (MRI) performance parameters is an important means to ensure the image quality and the reliability of diagnosis results. There are some problems in the manual calculation and eye recognition of the quality inspection parameters, such as strong subjectivity and low efficiency. In view of these facts, an automatic analysis system for MRI quality detection based on QT is proposed and implemented in C++ language. The image processing algorithm is introduced to automatically measure and calculate the quality inspection parameters. The software with comprehensive functions is designed to systematically manage the quality inspection information of MRI. The experimental results show that the automatically calculated parameters are consistent with the manually calculated ones. Accordingly, the accuracy and reliability of the algorithm is verified. The whole system is efficient, convenient and easy to operate, and it can meet the actual needs of MRI quality inspection.

3DMorph Automatic Analysis of Microglial Morphology in Three Dimensions from Ex Vivo and In Vivo Imaging

Microglia are dynamic immune cells of the central nervous system, and their morphology is commonly used as a readout of cellular function. However, current morphological analysis techniques rely on either tracing of cells or two-dimensional projection analysis, which are time-consuming, subject to bias, and may ignore important three-dimensional (3D) information. Therefore, we have created 3DMorph, a MATLAB-based script that analyzes microglial morphology from 3D data. The program initially requires input of threshold levels, cell size expectations, and preferred methods of skeletonization. This makes 3DMorph easily scalable and adaptable to different imaging parameters or cell types. After these settings are defined, the program is completely automatic and can batch process files without user input. Output data includes cell volume, territorial volume, branch length, number of endpoints and branch points, and average distance between cells. We show that 3DMorph is accurate compared to manual tracing, with significantly decreased user input time. Importantly, 3DMorph is capable of processing in vivo microglial morphology, as well as other 3D branching cell types, from mouse cranial windows or acute hippocampal slices. Therefore, we present a novel, user-friendly, scalable, and semiautomatic method of analyzing cell morphology in 3 dimensions. This method should improve the accuracy of cell measurements, remove user bias between conditions, increase reproducibility between experimenters and labs, and reduce user input time. We provide this open source code on GitHub so that it is free and accessible to all investigators.

Relationship of annual change in bone mineral density with extent of emphysematous lesions and pulmonary function in patients with COPD

Background

Osteoporosis is a well-known comorbidity in COPD. It is associated with poor health status and prognosis. Although the exact pathomechanisms are unclear, osteoporosis is suggested to be either a comorbidity due to shared risk factors with COPD or a systematic effect of COPD with a cause-effect relationship. This study aimed to evaluate whether progression of osteoporosis is synchronized with that of COPD.

Materials and methods

Data from 103 patients with COPD included in the Hokkaido COPD cohort study were analyzed. Computed tomography (CT) attenuation values of thoracic vertebrae 4, 7, and 10 were measured using custom software, and the average value (average bone density; ABD_4,7,10) was calculated. The percentage of low attenuation volume (LAV%) for each patient was also calculated for evaluation of emphysematous lesions. Annual change in thoracic vertebral CT attenuation, which is strongly correlated with dual-energy X-ray absorptiometry-measured bone mineral density, was compared with that in FEV_1.0 or emphysematous lesions.

Results

In the first CT data set, ABD_4,7,10 was significantly correlated with age (ρ=-0.331; p=0.0006), body mass index (BMI; ρ=0.246; p=0.0136), St George's Respiratory Questionnaire (SGRQ) activity score (ρ=-0.248; p=0.0115), eosinophil count (ρ=0.229; p=0.0198), and LAV% (ρ=-0.372; p=0.0001). However, ABD_4,7,10 was not associated with FEV_1.0. After adjustment for age, BMI, SGRQ activity score, and eosinophil count, no significant relationship was found between ABD_4,7,10 and LAV%. Annual change in ABD_4,7,10 was not associated with annual change in LAV% or FEV_1.0.

Conclusion

Progression of osteoporosis and that of COPD are not directly related or synchronized with each other.

Automatic analysis of corneal nerves imaged using in vivo confocal microscopy

Interest has grown over the past decade in using in vivo confocal microscopy to analyse the morphology of corneal nerves and their changes over time. Advances in computational modelling techniques have been applied to automate the estimation of sub-basal nerve structure. These objective methods have the potential to quantify nerve density (and length), tortuosity, variations in nerve thickness, as well as temporal changes in nerve fibres such as migration patterns. Different approaches to automated nerve analysis, methods proposed and how they were validated in previous literature are reviewed. Improved understanding of these approaches and their limitations will help improve the diagnostic leverage of emerging developments for monitoring the onset and progression of a broad class of systemic diseases, including diabetes.

Supervised and Unsupervised Learning Technology in the Study of Rodent Behavior

Quantifying behavior is a challenge for scientists studying neuroscience, ethology, psychology, pathology, etc. Until now, behavior was mostly considered as qualitative descriptions of postures or labor intensive counting of bouts of individual movements. Many prominent behavioral scientists conducted studies describing postures of mice and rats, depicting step by step eating, grooming, courting, and other behaviors. Automated video assessment technologies permit scientists to quantify daily behavioral patterns/routines, social interactions, and postural changes in an unbiased manner. Here, we extensively reviewed published research on the topic of the structural blocks of behavior and proposed a structure of behavior based on the latest publications. We discuss the importance of defining a clear structure of behavior to allow professionals to write viable algorithms. We presented a discussion of technologies that are used in automated video assessment of behavior in mice and rats. We considered advantages and limitations of supervised and unsupervised learning. We presented the latest scientific discoveries that were made using automated video assessment. In conclusion, we proposed that the automated quantitative approach to evaluating animal behavior is the future of understanding the effect of brain signaling, pathologies, genetic content, and environment on behavior.

Automatic ¹H-NMR Screening of Fatty Acid Composition in Edible Oils

In this work, we introduce an NMR-based screening method for the fatty acid composition analysis of edible oils. We describe the evaluation and optimization needed for the automated analysis of vegetable oils by low-field NMR to obtain the fatty acid composition (FAC). To achieve this, two scripts, which automatically analyze and interpret the spectral data, were developed. The objective of this work was to drive forward the automated analysis of the FAC by NMR. Due to the fact that this protocol can be carried out at low field and that the complete process from sample preparation to printing the report only takes about 3 min, this approach is promising to become a fundamental technique for high-throughput screening. To demonstrate the applicability of this method, the fatty acid composition of extra virgin olive oils from various Spanish olive varieties (arbequina, cornicabra, hojiblanca, manzanilla, and picual) was determined by ¹H-NMR spectroscopy according to this protocol.

Automatic quantitative analysis of t-tubule organization in cardiac myocytes using ImageJ

The transverse tubule system in mammalian striated muscle is highly organized and contributes to optimal and homogeneous contraction. Diverse pathologies such as heart failure and atrial fibrillation include disorganization of t-tubules and contractile dysfunction. Few tools are available for the quantification of the organization of the t-tubule system. We developed a plugin for the ImageJ/Fiji image analysis platform developed by the National Institutes of Health. This plugin (TTorg) analyzes raw confocal microscopy images. Analysis options include the whole image, specific regions of the image (cropping), and z-axis analysis of the same image. Batch analysis of a series of images with identical criteria is also one of the options. There is no need to either reorientate any specimen to the horizontal or to do a thresholding of the image to perform analysis. TTorg includes a synthetic "myocyte-like" image generator to test the plugin's efficiency in the user's own experimental conditions. This plugin was validated on synthetic images for different simulated cell characteristics and acquisition parameters. TTorg was able to detect significant differences between the organization of the t-tubule systems in experimental data of mouse ventricular myocytes isolated from wild-type and dystrophin-deficient mice. TTorg is freely distributed, and its source code is available. It provides a reliable, easy-to-use, automatic, and unbiased measurement of t-tubule organization in a wide variety of experimental conditions.

An automated sleep-analysis system operated through a standard hospital monitor

STUDY OBJECTIVES

Sleep disordered breathing (SDB), a cause of clinically important cardiovascular comorbidity, is often not recognized and diagnosed. An automated system that detects SDB using signals from a standard hospital monitor might provide useful information about the presence and severity of SDB without the need to evaluate the patient in a sleep laboratory and without additional hardware. The aim of this study was to examine the feasibility and accuracy of routine overnight sleep testing for SDB detection by an automated analysis system that operates by analyzing signals derived from standard hospital monitors.

METHODS

Comparison of SDB detection by simultaneous "gold-standard" polysomnography and by Morpheus Hx (WideMed, Ltd., Herzliya, Israel), a bedside computerized analysis system (CAS) connected to a standard hospital monitor (ECG, respiratory impedance, end-tidal carbon dioxide (ETCO2), and SpO2). A total of 53 subjects were examined, 36 men and 17 women, all with suspected SDB. Each subject underwent an overnight sleep study, scored both by polysomnography and by CAS. The study was conducted in Brigham and Women's Hospital, Newton Center, MA.

RESULTS

CAS-derived values for apnea-hypopnea index and total sleep time, were each found to be highly correlated with the corresponding polysomnography results, with linear regression values of r = 0.96 and r = 0.82, respectively. Mean apnea-hypopnea index values were also quite similar (CAS of 15.5 +/- 20.0 vs polysomnography of 15.4 +/- 24.0).

CONCLUSIONS

An automated sleep-analysis system utilizing signals derived from a standard hospital monitor can be considered as a feasible and accurate method to detect and quantify SDB.

Rethinking sleep analysis

Visual sleep scoring is the obligatory reference for sleep analysis. An essential step in sleep scoring is sleep staging. This technique was first described in 1937 and later adapted 3 times: first, in 1957, after the detection of rapid eye movement (REM) sleep, when electrooculography (EOG) was added; second, in 1968, when sleep staging was standardized and electromyography (EMG) was added; and third, in 2007, to integrate accumulated knowledge from sleep science, adding arousals and respiratory, cardiac, and movement events. In spite of the dramatic changes that have taken place in recording and storing techniques, sleep staging has undergone surprisingly few changes. The argument of the present comment is that sleep staging was appropriate as long as sleep biosignals were recorded in the analog mode as curves on paper, whereas this staging may be insufficient for digitally recorded and stored sleep data. Limitations of sleep staging are critically discussed and alternative strategies of sleep analysis are emphasized.

New enzymatic assay for serum urea nitrogen using urea amidolyase

We established an enzymatic assay for measurement of serum urea nitrogen using urea amidolyase (EC 3.5.1.45) from yeast species. The method is based on hydrolysis of urea by the enzyme. In this assay, we eliminated endogenous ammonium ion by use of glutamate dehydrogenase (EC 1.4.1.4). Then in the presence of urea amido-lyase, ATP, bicarbonate, magnesium, and potassium ions, ammonium ion was produced proportionally to urea concentration in serum. The concentra-tion of ammonium ion formed was determined by adding GLDH to produce NADP(+) in the presence of 2-oxoglutarate and NADPH. We then monitored the change of absorbance at 340 nm. The inhibitory effect of calcium ion on this assay was eliminated by adding glyco-letherdiamine-N, N, N', N'-tetraacetic acid to the reaction system. The with-in-assay coefficient of variations (CVs) of the present method were 1.80-3.76% (n = 10) at 2.8-19.0 mmol/L, respectively. The day-to-day CVs were 2.23-4.59%. Analytical recovery was 92-115%. The presence of ascorbic acid, bilirubin, hemoglobin, lipemic material, ammo-nium ion, or calcium ion did not affect this assay system. The correlation be-tween values obtained with the present method (y) and those by another enzy-matic method (x) was 0.997 (y = 1.02x - 0.10 mmol/L, Sy/x = 0.841, n = 100), with a mean difference of -0.18 +/- 0.86 mmol/L [(values by reference method - that of present method) +/- SD] using the Bland-Altman technique. J. Clin. Lab. Anal. 17:52-56, 2003.

Automated tri-image analysis of stored corneal endothelium

BACKGROUND

Endothelial examination of organ culture stored corneas is usually done manually and on several mosaic zones. Some banks use an image analyser that takes account of only one zone. This method is restricted by image quality, and may be inaccurate if endothelial cell density (ECD) within the mosaic is not homogeneous. The authors have developed an analyser that has tools for automatic error detection and correction, and can measure ECD and perform morphometry on multiple zones of three images of the endothelial mosaic.

METHODS

60 human corneas were divided into two equal groups: group 1 with homogeneous mosaics, group 2 with heterogeneous ones. Three standard microscopy video images of the endothelium, graded by quality, were analysed either in isolation (so called mono-image analysis) or simultaneously (so called tri-image analysis), with 50 or 300 endothelial cells (ECs) counted. The automated analysis was compared with the manual analysis, which concerned 10 non-adjacent zones and about 300 cells. For each analysis method, failures and durations were studied according to image quality.

RESULTS

All corneas were able to undergo analysis, in about 2 or 7.5 minutes for 50 and 300 ECs respectively. The tri-image analysis did not increase analysis time and never failed, even with mediocre images. The tri-image analysis of 300 ECs was always most highly correlated with the manual count, particularly in the heterogeneous cornea group (r=0.94, p<0.001) and prevented serious count errors.

CONCLUSIONS

This analyser allows reliable and rapid analysis of ECD, even for heterogeneous endothelia mosaics and mediocre images.