Lobar sliding is an often-overlooked aspect of lung mechanics that presents a potential source of variance in the lung mechanics and physiology of different subjects. The goal of this study was to develop a finite element model suitable for studying lobar sliding in the lungs to gain insights about the extent to which too little or too much lobar sliding impacts breathing. We focused on the left lung as it has a single lobar fissure. The model geometry was derived from whole lung and lobar segmentations of a CT image at end inhalation and was divided into three components: a thoracic cavity outer shell, a volumetric upper lobe, and a volumetric lower lobe. Deformation of the lung tissue was driven by non-zero displacement boundary conditions applied to the thoracic cavity shell to simulate exhalation and contact constraints between each of the three geometric components. To verify the numerical methods used in this contact mechanics model, we replicated a contact mechanics benchmark problem from the literature. Further, numerical simulations were performed to determine the optimal contact mechanics parameters for the lung model. The resulting model displacement field exhibited discontinuities at the lobar fissure indicative of lobar sliding and had an average error of 2.3 mm when compared to displacements between anatomical landmarks on the CT images, which is consistent with lung simulation models in literature. The developed finite element contact mechanics model of the lung is novel in the lung mechanics field and may be interrogated to gain insights about the role of lobar sliding in breathing mechanics.

BACKGROUND AND OBJECTIVE:To compare the diagnostic performance of an autonomous diagnostic artificial intelligence (AI) system for the diagnosis of derivable diabetic retinopathy (RDR) with manual classification.MATERIALS AND METHODS:Patients with type 1 and type 2 diabetes participated in a diabetic retinopathy (DR) screening program between 2011-2012. 2 images of each eye were collected. Unidentifiable retinal images were obtained, one centered on the disc and one on the fovea. The exams were classified with the autonomous AI system and manually by anonymous ophthalmologists. The results of the AI system and manual classification were compared in terms of sensitivity and specificity for the diagnosis of both (RDR) and diabetic retinopathy with decreased vision (VTDR).RESULTS:10,257 retinal inages of 5,630 eyes of 2,680 subjects were included. According to the manual classification, the prevalence of RDR was 4.14% and that of VTDR 2.57%. The AI system recorded 100% (95% CI: 97-100%) sensitivity and 81.82% (95% CI: 80 -83%) specificity for RDR, and 100% (95% CI: 95-100%) of sensitivity and 94.64% (95% CI: 94-95%) of specificity for VTDR.CONCLUSIONS:Compared to the manual classification, the autonomous diagnostic AI system registered a high sensitivity (100%) and specificity (82%) in the diagnosis of RDR and macular edema in people with diabetes. Due to its immediate diagnosis, the autonomous diagnostic AI system can increase the accessibility of RDR screening in primary care settings.

Purpose: The purpose of this study is to compare the diagnostic performance of an autonomous artificial intelligence (AI) system for the diagnosis of referable diabetic retinopathy (RDR) to manual grading by Spanish ophthalmologists. Methods: Subjects with type 1 and 2 diabetes participated in a diabetic retinopathy (DR) screening program in 2011 to 2012 in Valencia (Spain), and two images per eye were collected according to their standard protocol. Mydriatic drops were used in all patients. Retinal images—one disc and one fovea centered—were obtained under the Medical Research Ethics Committee approval and de-identified. Exams were graded by the autonomous AI system (IDx-DR, Coralville, Iowa, United States), and manually by masked ophthalmologists using adjudication. The outputs of the AI system and manual adjudicated grading were compared using sensitivity and specificity for diagnosis of both RDR and vision-threatening diabetic retinopathy (VTDR). Results: A total of 2680 subjects were included in the study. According to manual grading, prevalence of RDR was 111/2680 (4.14%) and of VTDR was 69/2680 (2.57%). Against manual grading, the AI system had a 100% (95% confidence interval [CI]: 97%-100%) sensitivity and 81.82% (95% CI: 80%-83%) specificity for RDR, and a 100% (95% CI: 95%-100%) sensitivity and 94.64% (95% CI: 94%-95%) specificity for VTDR. Conclusion: Compared to manual grading by ophthalmologists, the autonomous diagnostic AI system had high sensitivity (100%) and specificity (82%) for diagnosing RDR and macular edema in people with diabetes in a screening program. Because of its immediate, point of care diagnosis, autonomous diagnostic AI has the potential to increase the accessibility of RDR screening in primary care settings.

Comparar el rendimiento diagnóstico de un sistema de inteligencia artificial (IA) de diagnóstico autónomo para el diagnóstico de retinopatía diabética derivable (RDR) con la clasificación manual. Sujetos con diabetes tipo 1 y 2 participaron en un programa de cribado de retinopatía diabética (RD) entre 2011-2012. Se recogieron dos imágenes de cada ojo. Se obtuvieron imágenes retinianas no identificables, una centrada en el disco y otra en la fóvea. Los exámenes se clasificaron con el sistema de IA autónomo y manualmente por parte de oftalmólogos anónimos. Los resultados del sistema de IA y de la clasificación manual se compararon en cuanto a sensibilidad y especificidad para el diagnóstico tanto de RDR como de retinopatía diabética con disminución de la visión (VTDR). Se incluyeron 10.257 retinografías de 5.360 ojos de 2.680 sujetos. De acuerdo con la clasificación manual, la prevalencia de RDR fue de 4,14% y la de VTDR de 2,57%. El sistema de IA registró un 100% (95% IC: 97-100%) de sensibilidad y un 81,82% (95% IC: 80-83%) de especificidad para RDR, y un 100% (95% IC: 95-100%) de sensibilidad y un 94,64% (95% IC: 94-95%) de especificidad para VTDR. En comparación con la clasificación manual, el sistema de IA de diagnóstico autónomo registró una sensibilidad (100%) y una especificidad (82%) elevadas en el diagnóstico de RDR y edema macular en personas con diabetes. Debido a su diagnóstico inmediato, el sistema de IA de diagnóstico autónomo puede aumentar la accesibilidad al cribado de RDR en contextos de atención primaria. To compare the diagnostic performance of an autonomous diagnostic artificial intelligence (AI) system for the diagnosis of derivable diabetic retinopathy (RDR) with manual classification. Patients with type 1 and type 2 diabetes participated in a diabetic retinopathy (DR) screening program between 2011-2012. 2 images of each eye were collected. Unidentifiable retinal images were obtained, one centered on the disc and one on the fovea. The exams were classified with the autonomous AI system and manually by anonymous ophthalmologists. The results of the AI system and manual classification were compared in terms of sensitivity and specificity for the diagnosis of both (RDR) and diabetic retinopathy with decreased vision (VTDR). 10,257 retinal inages of 5,630 eyes of 2,680 subjects were included. According to the manual classification, the prevalence of RDR was 4.14% and that of VTDR 2.57%. The AI system recorded 100% (95% CI: 97-100%) sensitivity and 81.82% (95% CI: 80 -83%) specificity for RDR, and 100% (95% CI: 95-100%) of sensitivity and 94.64% (95% CI: 94-95%) of specificity for VTDR. Compared to the manual classification, the autonomous diagnostic AI system registered a high sensitivity (100%) and specificity (82%) in the diagnosis of RDR and macular edema in people with diabetes. Due to its immediate diagnosis, the autonomous diagnostic AI system can increase the accessibility of RDR screening in primary care settings.

Deep learning algorithms, typically implemented as Convolutional Neural Networks (CNNs), in recent years have gained traction in medical image analysis. The majority of CNNs employed in retinal image diagnosis applications are image-based; wherein input is the retinal image and output is the classification/diagnosis, resulting in a black-box like algorithm. In contrast, hybrid lesion-based algorithms employ multiple CNN-based detectors to categorically detect various lesions in the image, and final diagnosis is computed from combination of detector outputs. Such algorithms are more physiologically plausible and provides explainability of the final prediction through intermediate detector outputs. Both classes of algorithms have reported equal diagnostic performance and outperform clinical experts in detecting referable diabetic retinopathy (rDR). However, CNNs are sensitive to adversarial images where a limited number of pixels are modified by a fraction of intensity while preserving global image context; leading to CNN misclassification. We compared diagnostic accuracy of the two classes of algorithms on adversarial images generated from rDR retinal images and results show that image-based CNNs are significantly more susceptible to adversarial images than hybrid lesion-based algorithms.

Osteoporosis is a common bone disease associated with reduced bone strength and increased fracture risk. Finite element modelling (FEM) is a powerful tool to assess bone strength. Fast acquisition and highly reduced radiation in latest multi-row detector CT (MDCT) put it as a frontline imaging modality to assess in vivo trabecular bone (TB) microarchitecture and strength. In current CT image resolution, conventional segmentation methods fail to maintain TB network connectivity limiting reliable assessment of TB strength using FEM. In this paper, we present a new space-variant hysteresis approach to maintain TB connectivity while preserving marrow pores and a high quality mesh generator for FEM. We examine the effectiveness in estimating TB strength. The reproducibility and the ability to predict actual bone strength were examined on MDCT images of cadaveric ankle specimens under in vivo conditions. An intra-class correlation coefficient of 0.97 was observed in computed Young's modulus from repeat scans, and a high linear correlation (R2=0.92) were found between computed and experimental Young's modulus.

PURPOSE. To compare performance of a deep-learning enhanced algorithm for automated detection of diabetic retinopathy (DR), to the previously published performance of that algorithm, the Iowa Detection Program (IDP)-without deep learning components-on the same publicly available set of fundus images and previously reported consensus reference standard set, by three US Board certified retinal specialists.METHODS. We used the previously reported consensus reference standard of referable DR (rDR), defined as International Clinical Classification of Diabetic Retinopathy moderate, severe nonproliferative (NPDR), proliferative DR, and/or macular edema (ME). Neither Messidor-2 images, nor the three retinal specialists setting the Messidor-2 reference standard were used for training IDx-DR version X2.1. Sensitivity, specificity, negative predictive value, area under the curve (AUC), and their confidence intervals (CIs) were calculated.RESULTS. Sensitivity was 96.8% (95% CI: 93.3%-98.8%), specificity was 87.0% (95% CI: 84.2%-89.4%), with 6/874 false negatives, resulting in a negative predictive value of 99.0% (95% CI: 97.8%-99.6%). No cases of severe NPDR, PDR, or ME were missed. The AUC was 0.980 (95% CI: 0.968-0.992). Sensitivity was not statistically different from published IDP sensitivity, which had a CI of 94.4% to 99.3%, but specificity was significantly better than the published IDP specificity CI of 55.7% to 63.0%.CONCLUSIONS. A deep-learning enhanced algorithm for the automated detection of DR, achieves significantly better performance than a previously reported, otherwise essentially identical, algorithm that does not employ deep learning. Deep learning enhanced algorithms have the potential to improve the efficiency of DR screening, and thereby to prevent visual loss and blindness from this devastating disease.

Purpose: Osteoporosis is a common bone disease associated with increased risk of low-trauma fractures leading to substantial morbidity, mortality, and financial costs. Clinically, osteoporosis is defined by low bone mineral density (BMD); however, increasing evidence suggests that trabecular bone (TB) microarchitectural quality is an important determinant of bone strength and fracture risk. A tensor scale based algorithm for in vivo characterization of TB plate-rod microarchitecture at the distal tibia using multirow detector CT (MD-CT) imaging is presented and its performance and applications are examined.Methods: The tensor scale characterizes individual TB on the continuum between a perfect plate and a perfect rod and computes their orientation using optimal ellipsoidal representation of local structures. The accuracy of the method was evaluated using computer-generated phantom images at a resolution and signal-to-noise ratio achievable in vivo. The robustness of the method was examined in terms of stability across a wide range of voxel sizes, repeat scan reproducibility, and correlation between TB measures derived by imaging human ankle specimens under ex vivo and in vivo conditions. Finally, the application of the method was evaluated in pilot human studies involving healthy young-adult volunteers (age: 19 to 21 yr; 51 females and 46 males) and patients treated with selective serotonin reuptake inhibitors (SSRIs) (age: 19 to 21 yr; six males and six females).Results: An error of (3.2%+/- 2.0%) (mean +/- SD), computed as deviation from known measures of TB plate-width, was observed for computer-generated phantoms. An intraclass correlation coefficient of 0.95 was observed for tensor scale TB measures in repeat MD-CT scans where the measures were averaged over a small volume of interest of 1.05 mm diameter with limited smoothing effects. The method was found to be highly stable at different voxel sizes with an error of (2.29%+/- 1.56%) at an in vivo voxel size as compared to the original ex vivo voxel size. Tensor scale measures derived from imaging under in vivo and ex vivo conditions with significantly different modulation transfer function, i.e., difference in "true resolution," showed strong linear correlation (r = 0.92). The study of healthy volunteers shows that, after adjustment for height and weight, males have a 14% higher mean TB plate-width as compared to females (p < 0.05). SSRI-treated patients have 12.5% lower mean TB plate-width (p = 0.052) as compared to age-similar and sex-, height-, and weight-matched healthy controls. In contrast, the observed group difference in dual-energy x-ray absorptiometry (DXA)-derived hip BMD was 10.5% between males and females and only 5.04% between healthy controls and patients on SSRIs.Conclusions: Tensor scale analysis of MD-CT images yields accurate and reproducible characterization of TB plate-rod microarchitecture that may be more sensitive than DXA-derived BMD to sex differences and to the skeletal changes associated with medical conditions or their treatments. (C) 2015 American Association of Physicists in Medicine.

ContextSpinal cord injury (SCI) causes a decline of bone mineral density (BMD) in the paralyzed extremities via the gradual degradation and resorption of trabecular elements. Clinical tools that report BMD may not offer insight into trabecular architecture flaws that could affect bone's ability to withstand loading. We present a case of a woman with a 30-year history of SCI and abnormally high distal femur BMD.FindingsPeripheral quantitative-computed tomography-based BMD for this subject was ∼20% higher than previously published non-SCI values. Computed tomography (CT) revealed evidence of sclerotic bone deposition in the trabecular envelope, most likely due to glucocorticoid-induced osteonecrosis. Volumetric topologic analysis of trabecular architecture indicated that the majority of the bone mineral was organized into thick, plate-like structures rather than a multi-branched trabecular network. Visual analysis of the CT stack confirmed that the sclerotic bone regions were continuous with the cortex at only a handful of points.ConclusionsConventional clinical BMD analysis could have led to erroneous assumptions about this subject's bone quality. CT-based analysis revealed that this subject's high BMD masked underlying architectural flaws. For patients who received prolonged glucocorticoid therapy, excessively high BMD should be viewed with caution. The ability of this subject's bone to resist fracture is, in our view, extremely suspect. A better understanding of the mechanical competency of this very dense, but architecturally flawed bone would be desirable before this subject engaged in activities that load the limbs.

The lobes of the lung slide relative to each other during breathing. Quantifying lobar sliding can aid in better understanding lung function, better modeling of lung dynamics, and for studying phenomenon such as pleural adhesion. We propose a novel measure to characterize lobe sliding in the lung based on the displacement field obtained from image registration of CT scans. When two sliding lobes are modeled as a continuum, the discontinuity in the displacement field at the fissure will manifest as elevated maximum shear—the proposed measure—which is capable of capturing both the level and orientation of sliding. Six human lungs were analyzed using scans spanning functional residual capacity to total lung capacity. The lung lobes were segmented and registered on a lobe-by-lobe basis to obtain the displacement field from which the proposed sliding measure was calculated. The sliding measure was found to be insignificant in the parenchyma, as relatively little tissue shear occurs here. On the other hand, it was elevated along the fissures. Thus, a map of the proposed sliding measure of the entire lung clearly delineates and quantifies sliding between lung lobes. Sliding is a key aspect of lung deformation during breathing. The proposed measure may help resolve artifacts introduced by sliding in deformation analysis techniques used for radiotherapy.

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MOTIVATION: The lobes of the lungs slide relative to each other during breathing. Quantifying lobar sliding can aid in better understanding lung function, better modeling of lung dynamics, and a better understanding of the limits of image registration performance near fissures. We have developed a method to estimate lobar sliding in the lung from image registration of CT scans.METHODS: Six human lungs were analyzed using CT scans spanning functional residual capacity (FRC) to total lung capacity (TLC). The lung lobes were segmented and registered on a lobe-by-lobe basis. The displacement fields from the independent lobe registrations were then combined into a single image. This technique allows for displacement discontinuity at lobar boundaries. The displacement field was then analyzed as a continuum by forming finite elements from the voxel grid of the FRC image. Elements at a discontinuity will appear to have undergone significantly elevated 'shear stretch' compared to those within the parenchyma. Shear stretch is shown to be a good measure of sliding magnitude in this context.RESULTS: The sliding map clearly delineated the fissures of the lung. The fissure between the right upper and right lower lobes showed the greatest sliding in all subjects while the fissure between the right upper and right middle lobe showed the least sliding.

We have previously demonstrated the 24-hour redistribution and reabsorption of bronchoalveolar lavage (BAL) fluid delivered to the lung during a bronchoscopic procedure in normal volunteers. In this work we utilize image-matching procedures to correlate fluid redistribution and reabsorption to changes in regional lung function. Lung CT datasets from six human subjects were used in this study. Each subject was scanned at four time points before and after BAL procedure. Image registration was performed to align images at different time points and different inflation levels. The resulting dense displacement fields were utilized to track tissue volume changes and reveal deformation patterns of local parenchymal tissue quantitatively. The registration accuracy was assessed by measuring landmark matching errors, which were on the order of 1 mm. The results show that quantitative-assessed fluid volume agreed well with bronchoscopist-reported unretrieved BAL volume in the whole lungs (squared linear correlation coefficient was 0.81). The average difference of lung tissue volume at baseline and after 24 hours was around 2%, which indicates that BAL fluid in the lungs was almost absorbed after 24 hours. Regional lung-function changes correlated with the presence of BAL fluid, and regional function returned to baseline as the fluid was reabsorbed.

The deformation of the lung during inspiration and expiration involves regional variations in volume change and orientational preferences. Studies have reported techniques for measuring the displacement field in the lung based on imaging or image registration. However, means of interpreting all the information in the displacement field in a physiologically relevant manner is lacking. We propose three indices of lung deformation that are determinable from the displacement field: the Jacobian—a measure of volume change, the anisotropic deformation index—a measure of the magnitude of directional preference in volume change and a slab-rod index—a measure of the nature of directional preference in volume change. To demonstrate the utility of these indices, they were determined for six human subjects using deformable image registration on static CT images, registered from FRC to TLC. Volume change was elevated in the inferior-dorsal region as should be expected for breathing in the supine position. The anisotropic deformation index was elevated in the inferior region owing to proximity to the diaphragm and in the lobar fissures owing to sliding. Vessel regions in the lung had a significantly rod-like deformation compared to the whole lung. Compared to upper lobes, lower lobes exhibited significantly greater volume change (19.4% and 21.3% greater in the right and left lungs on average; p<0.005) and anisotropy in deformation (26.3% and 21.8% greater in the right and left lungs on average; p<0.05) with remarkable consistency across subjects. The developed deformation indices lend themselves to exhaustive and physiologically intuitive interpretations of the displacement fields in the lung determined through image-registration techniques or finite element simulations.

Characterizing the mechanical properties of anisotropic biological soft tissues poses unique challenges. One among these challenges is the lack of a priori information on the underlying fiber orientations that drive its anisotropic behavior. While fiber orientations in very thin planar tissues may be characterized by non-destructive means prior to mechanical testing, this is not possible with thick tissues. A planar radial extension test of circular planar biological soft tissue specimen is proposed for simultaneously ascertaining both the preferred fiber orientations and the mechanical properties. When pulled by a radial load, a circular specimen will deform into an elliptical shape revealing its stiffest direction (minor axis of ellipse) and its anisotropic properties. Numerical investigations were used to assess the stress and strain fields generated and justify the analytical equations to calculate stress and strain. A simple manual force-controlled planar radial extension device was constructed. A porcine aortic specimen was subjected to planar radial extension testing and the test data was fit to an anisotropic finite strain constitutive model to demonstrate the feasibility of this method for assessment of anisotropic biological soft tissues whose material symmetry is not known a priori.

Image registration plays an important role within pulmonary image analysis. Accurate registration is critical to post-analysis of lung mechanical properties. To improve registration accuracy, we utilize the rich information of vessel locations and shapes, and introduce a new similarity criterion, sum of squared vesselness measure difference (SSVMD). This metric is added to three existing intensity-based similarity criteria for nonrigid lung CT image registration to show its ability in improving matching accuracy. The registration accuracy is assessed by landmark error calculation and distance map visualization on vascular tree. The average landmark errors are reduced by over 20% and are within 0.7 mm after adding SSVMD constraint to three existing intensity-based similarity metrics. Visual inspection shows matching accuracy improvements in the lung regions near the thoracic cage and near the diaphragm. Experiments also show this vesselness constraint makes the Jacobian map of transformations physiologically more plausible and reliable.