Validity of prognostic models of critical COVID-19 is variable. A systematic review with external validation

OBJECTIVES

To identify prognostic models which estimate the risk of critical COVID-19 in hospitalized patients and to assess their validation properties.

STUDY DESIGN AND SETTING

We conducted a systematic review in Medline (up to January 2021) of studies developing or updating a model that estimated the risk of critical COVID-19, defined as death, admission to intensive care unit, and/or use of mechanical ventilation during admission. Models were validated in two datasets with different backgrounds (HM [private Spanish hospital network], n = 1,753, and ICS [public Catalan health system], n = 1,104), by assessing discrimination (area under the curve [AUC]) and calibration (plots).

RESULTS

We validated 18 prognostic models. Discrimination was good in nine of them (AUCs ≥ 80%) and higher in those predicting mortality (AUCs 65%-87%) than those predicting intensive care unit admission or a composite outcome (AUCs 53%-78%). Calibration was poor in all models providing outcome's probabilities and good in four models providing a point-based score. These four models used mortality as outcome and included age, oxygen saturation, and C-reactive protein among their predictors.

CONCLUSION

The validity of models predicting critical COVID-19 by using only routinely collected predictors is variable. Four models showed good discrimination and calibration when externally validated and are recommended for their use.

Domain expert evaluation of advanced visual computing solutions and 3D printing for the planning of the left atrial appendage occluder interventions

Advanced visual computing solutions and three-dimensional (3D) printing are moving from engineering to clinical pipelines for training, planning, and guidance of complex interventions. 3D imaging and rendering, virtual reality (VR), and in-silico simulations, as well as 3D printing technologies provide complementary information to better understand the structure and function of the organs, thereby improving and personalizing clinical decisions. In this study, we evaluated several advanced visual computing solutions, such as web-based 3D imaging visualization, VR, and computational fluid simulations, together with 3D printing, for the planning of the left atrial appendage occluder (LAAO) device implantations. Six cardiologists tested different technologies in pre-operative data of five patients to identify the usability, limitations, and requirements for the clinical translation of each technology through a qualitative questionnaire. The obtained results demonstrate the potential impact of advanced visual computing solutions and 3D printing to improve the planning of LAAO interventions as well as the need for their integration into a single workflow to be used in a clinical environment.

Predictors of long-term survival in patients undergoing residential cardiac rehabilitation (rCR) after transcatheter aortic valve replacement (TAVR): a multicenter retrospective study

Purpose

To evaluate exercise-based rCR derived outcome predictors in patients referred after TAVR.

Methods

Data of 232 patients (aged 82±6 years, 45%males) admitted to an average 3-week rCR program after TAVR (walking, up to 30 minutes of cycling or treadmill session twice daily, respiratory and calisthenic training) from January 2009 to December 2017 and home discharged, were retrospectively collected at 10 rCR Divisions of Istituti Clinici Scientifici Maugeri. Comorbidity (cumulative illness rated state-comorbidity index) (CIRS-CI) score, echocardiography on admission, disability (Barthel Index) (BI) score at discharge, six minutes walking test distance (6MWT) on discharge and maximal training session intensity (MTSI expressed in METs per minutes) were collected. All-cause mortality was assessed up to 3 years after rCR discharge.

Results

During a 3-year follow-up, there were 74 (32%) deaths. At univariate analysis, at discharge non survivors compared to survivors had significantly higher comorbidity rate (CIRS-ICC 5.2±2.3 vs 4.1±1.9, p=0.000) and disability level (BI 80.4±24 vs 88.8±17, p=0.000). Moreover, they had worse renal function (creatinine 1.6±0.9mg/dl vs 1.2±0.4, p=0.000), were more often on diuretic therapy (73% vs 53.2%, p=0.003) and on beta-blocker therapy (73% vs 57,6%, p=0.042) and had a markedly reduced functional capacity (6MWTD 221±100m vs 265±105m, p=0.001). At multivariate logistic stepwise analysis a lower comorbidity (CIRS-ICC), a more preserved renal function (creatinine level), a reduced use of diuretic therapy and 6MWT at discharge confirmed their power as independent predictors of survival at follow up (Table, Harrel's C = 0.707)

Conclusions

Patients attending rCR after TAVR are very old with significant comorbidity; overall mortality at 3-year follow-up after CR discharge is substantial. Our results suggest the need to implement ad-hoc long-term care strategies based on residual exercise capacity, comorbidity score and renal function for tailoring follow-up in patients discharged from rCR after TAVR.

Funding Acknowledgement

Type of funding sources: None. Table 1

Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound

Recent advances in fetal imaging open the door to enhanced detection of fetal disorders and computer-assisted surgical planning. However, precise segmentation of womb's tissues is challenging due to motion artifacts caused by fetal movements and maternal respiration during acquisition. This work aims to efficiently segment different intrauterine tissues in fetal magnetic resonance imaging (MRI) and 3D ultrasound (US). First, a large set of ninety-four radiomic features are extracted to characterize the mother uterus, placenta, umbilical cord, fetal lungs, and brain. The optimal features for each anatomy are identified using both K-best and Sequential Forward Feature Selection techniques. These features are then fed to a Support Vector Machine with instance balancing to accurately segment the intrauterine anatomies. To the best of our knowledge, this is the first time that "Radiomics" is expanded from classification tasks to segmentation purposes to deal with challenging fetal images. In addition, we evaluate several state-of-the-art deep learning-based segmentation approaches. Validation is extensively performed on a set of 60 axial MRI and 3D US images from pathological and clinical cases. Our results suggest that combining the selected 10 radiomic features per anatomy along with DeepLabV3+ or BiSeNet architectures for MRI, and PSPNet or Tiramisu for 3D US, can lead to the highest fetal / maternal tissue segmentation performance, robustness, informativeness, and heterogeneity. Therefore, this work opens new avenues for advancement of segmentation techniques and, in particular, for improved fetal surgical planning.

EView: An electric field visualization web platform for electroporation-based therapies

BACKGROUND AND OBJECTIVES

Electroporation is the phenomenon by which cell membrane permeability to ions and macromolecules is increased when the cell is briefly exposed to high electric fields. In electroporation-based treatments, such exposure is typically performed by delivering high voltage pulses across needle electrodes in tissue. For a given tissue and pulsing protocol, an electric field magnitude threshold exists that must be overreached for treatment efficacy. However, it is hard to preoperatively infer the treatment volume because the electric field distribution intricately depends on the electrodes' positioning and length, the applied voltage, and the electric conductivity of the treated tissues. For illustrating such dependencies, we have created EView (https://eview.upf.edu), a web platform that estimates the electric field distribution for arbitrary needle electrode locations and orientations and overlays it on 3D medical images.

METHODS

A client-server approach has been implemented to let the user set the electrode configuration easily on the web browser, whereas the simulation is computed on a dedicated server. By means of the finite element method, the electric field is solved in a 3D volume. For the sake of simplicity, only a homogeneous tissue is modeled, assuming the same properties for healthy and pathologic tissues. The non-linear dependence of tissue conductivity on the electric field due to the electroporation effect is modeled. The implemented model has been validated against a state of the art finite element solver, and the server has undergone a heavy load test to ensure reliability and to report execution times.

RESULTS

The electric field is rapidly computed for any electrode and tissue configuration, and alternative setups can be easily compared. The platform provides the same results as the state of the art finite element solver (Dice = 98.3 ± 0.4%). During the high load test, the server remained responsive. Simulations are computed in less than 2 min for simple cases consisting of two electrodes and take up to 40 min for complex scenarios consisting of 6 electrodes.

CONCLUSIONS

With this free platform we provide expert and non-expert electroporation users a way to rapidly model the electric field distribution for arbitrary electrode configurations.

Residential cardiac rehabilitation (rCR) derived survival predictors in patients after transcatheter aortic valve replacement (TAVR): a retrospective multicenter study

Purpose

To evaluate exercise-based rCR derived outcome predictors in patients referred after TAVR.

Methods

Data of 434 patients (aged 81±6 years) admitted to an average 3-week rCR program after TAVR (walking, up to 30 minutes of cycling or treadmill session twice daily, respiratory and calistenic training) from January 2009 to December 2017 and home discharged, were retrospectively collected at 10 Italian rCR Division of Istituti Clinici Maugeri SpA. Comorbidity (cumulative illness rated state-comorbidity index) (CIRS-CI) score, echocardiography on admission, Disability (Barthel Index) (BI) score, Morse Fall Scale score (MFS), six minutes walking test distance (6MWT) on admission and discharge and maximal training session intensity (MTSI expressed in METs per minutes) were collected. The mortality was assessed up to 3 years after rCR discharge.

Results

During a 3-years follow up there were 120 (28%) deaths. At t-test analysis non survivors compared to survivors had significantly higher CIRS CI (p=0.000), MFS score on admission (p=0.008) and discharge (p=0.017), serum creatinine level on admission (p=0.000) and discharge (p=0.000); moreover they had significantly lower BI score on admission (p=0.000) and discharge (P=0.000), left ventricle ejection fraction (p=0.008),6MWT on admission (p=0.001) and discharge (p=0.000) and MTSI (p=0.022) in comparison to survivors.

At multivariate logistic stepwise analysis, BI score on admission and serum creatinine level at discharge were the only independent predictors of mortality (Table 1); the AUC of the final logistic model was 0.72.

Conclusions

Patients attending rCR after TAVR seem to be very old; overall mortality at 3 years follow up in patients discharged home after rCR is substantial. Disability profile on admission (measured by Barthel Index) and impaired renal function on discharge (measured by creatinine levels) are independently correlated to death at long term follow up.

Funding Acknowledgement

Type of funding source: None

TTTS-STgan: Stacked Generative Adversarial Networks for TTTS Fetal Surgery Planning Based on 3D Ultrasound

Twin-to-twin transfusion syndrome (TTTS) is characterized by an unbalanced blood transfer through placental abnormal vascular connections. Prenatal ultrasound (US) is the imaging technique to monitor monochorionic pregnancies and diagnose TTTS. Fetoscopic laser photocoagulation is an elective treatment to coagulate placental communications between both twins. To locate the anomalous connections ahead of surgery, preoperative planning is crucial. In this context, we propose a novel multi-task stacked generative adversarial framework to jointly learn synthetic fetal US generation, multi-class segmentation of the placenta, its inner acoustic shadows and peripheral vasculature, and placenta shadowing removal. Specifically, the designed architecture is able to learn anatomical relationships and global US image characteristics. In addition, we also extract for the first time the umbilical cord insertion on the placenta surface from 3D HD-flow US images. The database consisted of 70 US volumes including singleton, mono- and dichorionic twins at 17-37 gestational weeks. Our experiments show that 71.8% of the synthesized US slices were categorized as realistic by clinicians, and that the multi-class segmentation achieved Dice scores of 0.82 ± 0.13, 0.71 ± 0.09, and 0.72 ± 0.09, for placenta, acoustic shadows, and vasculature, respectively. Moreover, fetal surgeons classified 70.2% of our completed placenta shadows as satisfactory texture reconstructions. The umbilical cord was successfully detected on 85.45% of the volumes. The framework developed could be implemented in a TTTS fetal surgery planning software to improve the intrauterine scene understanding and facilitate the location of the optimum fetoscope entry point.

Re-Identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks

Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists. Several lung CT scan images taken at different time points of a given patient need to be individually inspected, looking for possible cancerogenous nodules. Radiologists mainly focus their attention in nodule size, density, and growth to assess the existence of malignancy. In this study, we present a novel method based on a 3D siamese neural network, for the re-identification of nodules in a pair of CT scans of the same patient without the need for image registration. The network was integrated into a two-stage automatic pipeline to detect, match, and predict nodule growth given pairs of CT scans. Results on an independent test set reported a nodule detection sensitivity of 94.7%, an accuracy for temporal nodule matching of 88.8%, and a sensitivity of 92.0% with a precision of 88.4% for nodule growth detection.

Deep Q-CapsNet Reinforcement Learning Framework for Intrauterine Cavity Segmentation in TTTS Fetal Surgery Planning

Fetoscopic laser photocoagulation is the most effective treatment for Twin-to-Twin Transfusion Syndrome, a condition affecting twin pregnancies in which there is a deregulation of blood circulation through the placenta, that can be fatal to both babies. For the purposes of surgical planning, we design the first automatic approach to detect and segment the intrauterine cavity from axial, sagittal and coronal MRI stacks. Our methodology relies on the ability of capsule networks to successfully capture the part-whole interdependency of objects in the scene, particularly for unique class instances (i.e., intrauterine cavity). The presented deep Q-CapsNet reinforcement learning framework is built upon a context-adaptive detection policy to generate a bounding box of the womb. A capsule architecture is subsequently designed to segment (or refine) the whole intrauterine cavity. This network is coupled with a strided nnU-Net feature extractor, which encodes discriminative feature maps to construct strong primary capsules. The method is robustly evaluated with and without the localization stage using 13 performance measures, and directly compared with 15 state-of-the-art deep neural networks trained on 71 singleton and monochorionic twin pregnancies. An average Dice score above 0.91 is achieved for all ablations, revealing the potential of our approach to be used in clinical practice.

Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning

PURPOSE

Twin-to-twin transfusion syndrome (TTTS) is a serious condition that occurs in about 10-15% of monochorionic twin pregnancies. In most instances, the blood flow is unevenly distributed throughout the placenta anastomoses leading to the death of both fetuses if no surgical procedure is performed. Fetoscopic laser coagulation is the optimal therapy to considerably improve co-twin prognosis by clogging the abnormal anastomoses. Notwithstanding progress in recent years, TTTS surgery is highly risky. Computer-assisted planning of the intervention can thus improve the outcome.

METHODS

In this work, we implement a GPU-accelerated random walker (RW) algorithm to detect the placenta, both umbilical cord insertions and the placental vasculature from Doppler ultrasound (US). Placenta and background seeds are manually initialized in 10-20 slices (out of 245). Vessels are automatically initialized in the same slices by means of Otsu thresholding. The RW finds the boundaries of the placenta and reconstructs the vasculature.

RESULTS

We evaluate our semiautomatic method in 5 monochorionic and 24 singleton pregnancies. Although satisfactory performance is achieved on placenta segmentation (Dice ≥ 84.0%), some vascular connections are still neglected due to the presence of US reverberation artifacts (Dice ≥ 56.9%). We also compared inter-user variability and obtained Dice coefficients of ≥ 76.8% and ≥ 97.42% for placenta and vasculature, respectively. After a 3-min manual initialization, our GPU approach speeds the computation 10.6 times compared to the CPU.

CONCLUSIONS

Our semiautomatic method provides a near real-time user experience and requires short training without compromising the segmentation accuracy. A powerful approach is thus presented to rapidly plan the fetoscope insertion point ahead of TTTS surgery.

Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline

The early identification of malignant pulmonary nodules is critical for a better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules; METHODS: In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection. For training and testing purposes we used independent subsets of the LIDC dataset; RESULTS: Adding the probabilities of nodules malignity in a baseline lung cancer pipeline improved its F1-weighted score by 14.7%, whereas integrating the malignancy model itself using transfer learning outperformed the baseline prediction by 11.8% of F1-weighted score; CONCLUSIONS: Despite the limited size of the lung cancer datasets, integrating predictive models of nodule malignancy improves prediction of lung cancer.

Fetal MRI Synthesis via Balanced Auto-Encoder Based Generative Adversarial Networks

Machine learning approaches for image analysis require large amounts of training imaging data. As an alternative, the use of realistic synthetic data reduces the high cost associated to medical image acquisition, as well as avoiding confidentiality and privacy issues, and consequently allows the creation of public data repositories for scientific purposes. Within the context of fetal imaging, we adopt an auto-encoder based Generative Adversarial Network for synthetic fetal MRI generation. The proposed architecture features a balanced power of the discriminator against the generator during training, provides an approximate convergence measure, and enables fast and robust training to generate high-quality fetal MRI in axial, sagittal and coronal planes. We demonstrate the feasibility of the proposed approach quantitatively and qualitatively by segmenting relevant fetal structures to assess the anatomical fidelity of the simulation, and performing a clinical verisimilitude study distinguishing the simulated data from the real images. The results obtained so far are promising, which makes further investigation on this new topic worthwhile.

TTTS-GPS: Patient-specific preoperative planning and simulation platform for twin-to-twin transfusion syndrome fetal surgery

Twin-to-twin transfusion syndrome (TTTS) is a serious condition that may occur in pregnancies when two or more fetuses share the same placenta. It is characterized by abnormal vascular connections in the placenta that cause blood to flow unevenly between the babies. If left untreated, perinatal mortality occurs in 90% of cases, whilst neurological injuries are still present in TTTS survivors. Minimally invasive fetoscopic laser surgery is the standard and optimal treatment for this condition, but is technically challenging and can lead to complications. Acquiring and maintaining the required surgical skills need consistent practice, and a steep learning curve. An accurate preoperative planning is thus vital for complex TTTS cases. To this end, we propose the first TTTS fetal surgery planning and simulation platform. The soft tissue of the mother, the uterus, the umbilical cords, the placenta and its vascular tree are segmented and registered automatically from magnetic resonance imaging and 3D ultrasound using computer vision and deep learning techniques. The proposed state-of-the-art technology is integrated into a flexible C++ and MITK-based application to provide a full exploration of the intrauterine environment by simulating the fetoscope camera as well as the laser ablation, determining the correct entry point, training doctors' movements and trajectory ahead of operation, which allows improving upon current practice. A comprehensive usability study is reported. Experienced surgeons rated highly our TTTS planner and simulator, thus being a potential tool to be implemented in real and complex TTTS surgeries.

3D patient-specific finite element models of the proximal femur based on DXA towards the classification of fracture and non-fracture cases

Osteoporotic bone fractures reduce quality of life and drastically increase mortality. Minimally irradiating imaging techniques such as dual-energy X-ray absorptiometry (DXA) allow assessment of bone loss through the use of bone mineral density (BMD) as descriptor. Yet, the accuracy of fracture risk predictions remains limited. Recently, DXA-based 3D modelling algorithms were proposed to analyse the geometry and BMD spatial distribution of the proximal femur. This study hypothesizes that such approaches can benefit from finite element (FE)-based biomechanical analyses to improve fracture risk prediction. One hundred and eleven subjects were included in this study and stratified in two groups: (a) 62 fracture cases, and (b) 49 non-fracture controls. Side fall was simulated using a static peak load that depended on patient mass and height. Local mechanical fields were calculated based on relationships between tissue stiffness and BMD. The area under the curve (AUC) of the receiver operating characteristic method evaluated the ability of calculated biomechanical descriptors to discriminate fracture and control cases. The results showed that the major principal stress was better discriminator (AUC > 0.80) than the volumetric BMD (AUC ≤ 0.70). High discrimination capacity was achieved when the analysis was performed by bone type, zone of fracture and gender/sex (AUC of 0.91 for women, trabecular bone and trochanter area), and outcomes suggested that the trabecular bone is critical for fracture discrimination. In conclusion, 3D FE models derived from DXA scans might significantly improve the prediction of hip fracture risk; providing a new insight for clinicians to use FE simulations in clinical practice for osteoporosis management.

Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI

Recent advances in fetal magnetic resonance imaging (MRI) open the door to improved detection and characterization of fetal and placental abnormalities. Since interpreting MRI data can be complex and ambiguous, there is a need for robust computational methods able to quantify placental anatomy (including its vasculature) and function. In this work, we propose a novel fully-automated method to segment the placenta and its peripheral blood vessels from fetal MRI. First, a super-resolution reconstruction of the uterus is generated by combining axial, sagittal and coronal views. The placenta is then segmented using 3D Gabor filters, texture features and Support Vector Machines. A uterus edge-based instance selection is proposed to identify the support vectors defining the placenta boundary. Subsequently, peripheral blood vessels are extracted through a curvature-based corner detector. Our approach is validated on a rich set of 44 control and pathological cases: singleton and (normal / monochorionic) twin pregnancies between 25-37 weeks of gestation. Dice coefficients of 0.82  ±  0.02 and 0.81  ±  0.08 are achieved for placenta and its vasculature segmentation, respectively. A comparative analysis with state of the art convolutional neural networks (CNN), namely, 3D U-Net, V-Net, DeepMedic, Holistic3D Net, HighRes3D Net and Dense V-Net is also conducted for placenta localization, with our method outperforming all CNN approaches. Results suggest that our methodology can aid the diagnosis and surgical planning of severe fetal disorders.

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects

Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Best prenatal diagnosis performances rely on the conscious preparation of the clinicians in terms of fetal anatomy knowledge. Therefore, fetal imaging will likely span and increase its prevalence in the forthcoming years. This review covers state-of-the-art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time. Potential applications of the aforementioned methods into clinical settings are also inspected. Finally, improvements in existing approaches as well as most promising avenues to new areas of research are briefly outlined.

Multi-scale immunological and biomechanical model of emphysema progression

This work presents a multi-scale agent-based model of emphysema progression that includes both the slow action of the immune system and the fast action of force redistribution and fracture propagation of the biological tissue. The two scales are coupled because the immune response causes inflammation and adaptation, which affects the biomechanical parameters of the tissue such as his elasticity. During repeated inflammation and breathing cycles, the tissue weakens and breaks down. We found that macrophages lifespan and cytokynes diffusion ratio are the parameters that influence the outcome of the model the most.

Computational Evaluation of Cochlear Implant Surgery Outcomes Accounting for Uncertainty and Parameter Variability

Cochlear implantation (CI) is a complex surgical procedure that restores hearing in patients with severe deafness. The successful outcome of the implanted device relies on a group of factors, some of them unpredictable or difficult to control. Uncertainties on the electrode array position and the electrical properties of the bone make it difficult to accurately compute the current propagation delivered by the implant and the resulting neural activation. In this context, we use uncertainty quantification methods to explore how these uncertainties propagate through all the stages of CI computational simulations. To this end, we employ an automatic framework, encompassing from the finite element generation of CI models to the assessment of the neural response induced by the implant stimulation. To estimate the confidence intervals of the simulated neural response, we propose two approaches. First, we encode the variability of the cochlear morphology among the population through a statistical shape model. This allows us to generate a population of virtual patients using Monte Carlo sampling and to assign to each of them a set of parameter values according to a statistical distribution. The framework is implemented and parallelized in a High Throughput Computing environment that enables to maximize the available computing resources. Secondly, we perform a patient-specific study to evaluate the computed neural response to seek the optimal post-implantation stimulus levels. Considering a single cochlear morphology, the uncertainty in tissue electrical resistivity and surgical insertion parameters is propagated using the Probabilistic Collocation method, which reduces the number of samples to evaluate. Results show that bone resistivity has the highest influence on CI outcomes. In conjunction with the variability of the cochlear length, worst outcomes are obtained for small cochleae with high resistivity values. However, the effect of the surgical insertion length on the CI outcomes could not be clearly observed, since its impact may be concealed by the other considered parameters. Whereas the Monte Carlo approach implies a high computational cost, Probabilistic Collocation presents a suitable trade-off between precision and computational time. Results suggest that the proposed framework has a great potential to help in both surgical planning decisions and in the audiological setting process.

Coupled Immunological and Biomechanical Model of Emphysema Progression

Chronic Obstructive Pulmonary Disease (COPD) is a disabling respiratory pathology, with a high prevalence and a significant economic and social cost. It is characterized by different clinical phenotypes with different risk profiles. Detecting the correct phenotype, especially for the emphysema subtype, and predicting the risk of major exacerbations are key elements in order to deliver more effective treatments. However, emphysema onset and progression are influenced by a complex interaction between the immune system and the mechanical properties of biological tissue. The former causes chronic inflammation and tissue remodeling. The latter influences the effective resistance or appropriate mechanical response of the lung tissue to repeated breathing cycles. In this work we present a multi-scale model of both aspects, coupling Finite Element (FE) and Agent Based (AB) techniques that we would like to use to predict the onset and progression of emphysema in patients. The AB part is based on existing biological models of inflammation and immunological response as a set of coupled non-linear differential equations. The FE part simulates the biomechanical effects of repeated strain on the biological tissue. We devise a strategy to couple the discrete biological model at the molecular /cellular level and the biomechanical finite element simulations at the tissue level. We tested our implementation on a public emphysema image database and found that it can indeed simulate the evolution of clinical image biomarkers during disease progression.

Patient-specific estimation of detailed cochlear shape from clinical CT images

PURPOSE

A personalized estimation of the cochlear shape can be used to create computational anatomical models to aid cochlear implant (CI) surgery and CI audio processor programming ultimately resulting in improved hearing restoration. The purpose of this work is to develop and test a method for estimation of the detailed patient-specific cochlear shape from CT images.

METHODS

From a collection of temporal bone [Formula: see text]CT images, we build a cochlear statistical deformation model (SDM), which is a description of how a human cochlea deforms to represent the observed anatomical variability. The model is used for regularization of a non-rigid image registration procedure between a patient CT scan and a [Formula: see text]CT image, allowing us to estimate the detailed patient-specific cochlear shape.

RESULTS

We test the accuracy and precision of the predicted cochlear shape using both [Formula: see text]CT and CT images. The evaluation is based on classic generic metrics, where we achieve competitive accuracy with the state-of-the-art methods for the task. Additionally, we expand the evaluation with a few anatomically specific scores.

CONCLUSIONS

The paper presents the process of building and using the SDM of the cochlea. Compared to current best practice, we demonstrate competitive performance and some useful properties of our method.

A multiscale imaging and modelling dataset of the human inner ear

Understanding the human inner ear anatomy and its internal structures is paramount to advance hearing implant technology. While the emergence of imaging devices allowed researchers to improve understanding of intracochlear structures, the difficulties to collect appropriate data has resulted in studies conducted with few samples. To assist the cochlear research community, a large collection of human temporal bone images is being made available. This data descriptor, therefore, describes a rich set of image volumes acquired using cone beam computed tomography and micro-CT modalities, accompanied by manual delineations of the cochlea and sub-compartments, a statistical shape model encoding its anatomical variability, and data for electrode insertion and electrical simulations. This data makes an important asset for future studies in need of high-resolution data and related statistical data objects of the cochlea used to leverage scientific hypotheses. It is of relevance to anatomists, audiologists, computer scientists in the different domains of image analysis, computer simulations, imaging formation, and for biomedical engineers designing new strategies for cochlear implantations, electrode design, and others.

A two dimensional electromechanical model of a cardiomyocyte to assess intra-cellular regional mechanical heterogeneities

Experimental studies on isolated cardiomyocytes from different animal species and human hearts have demonstrated that there are regional differences in the Ca2+ release, Ca2+ decay and sarcomere deformation. Local deformation heterogeneities can occur due to a combination of factors: regional/local differences in Ca2+ release and/or re-uptake, intra-cellular material properties, sarcomere proteins and distribution of the intracellular organelles. To investigate the possible causes of these heterogeneities, we developed a two-dimensional finite-element electromechanical model of a cardiomyocyte that takes into account the experimentally measured local deformation and cytosolic [Ca2+] to locally define the different variables of the constitutive equations describing the electro/mechanical behaviour of the cell. Then, the model was individualised to three different rat cardiac cells. The local [Ca2+] transients were used to define the [Ca2+]-dependent activation functions. The cell-specific local Young's moduli were estimated by solving an inverse problem, minimizing the error between the measured and simulated local deformations along the longitudinal axis of the cell. We found that heterogeneities in the deformation during contraction were determined mainly by the local elasticity rather than the local amount of Ca2+, while in the relaxation phase deformation was mainly influenced by Ca2+ re-uptake. Our electromechanical model was able to successfully estimate the local elasticity along the longitudinal direction in three different cells. In conclusion, our proposed model seems to be a good approximation to assess the heterogeneous intracellular mechanical properties to help in the understanding of the underlying mechanisms of cardiomyocyte dysfunction.

Computational Models for Predicting Outcomes of Neuroprosthesis Implantation: the Case of Cochlear Implants

Electrical stimulation of the brain has resulted in the most successful neuroprosthetic techniques to date: deep brain stimulation (DBS) and cochlear implants (CI). In both cases, there is a lack of pre-operative measures to predict the outcomes after implantation. We argue that highly detailed computational models that are specifically tailored for a patient can provide useful information to improve the precision of the nervous system electrode interface. We apply our framework to the case of CI, showing how we can predict nerve response for patients with both intact and degenerated nerve fibers. Then, using the predicted response, we calculate a metric for the usefulness of the stimulation protocol and use this information to rerun the simulations with better parameters.

Patient-specific simulation of implant placement and function for cochlear implantation surgery planning

We present a framework for patient specific electrical stimulation of the cochlea, that allows to perform in-silico analysis of implant placement and function before surgery. A Statistical Shape Model (SSM) is created from high-resolution human μCT data to capture important anatomical details. A Finite Element Model (FEM) is built and adapted to the patient using the results of the SSM. Electrical simulations based on Maxwell's equations for the electromagnetic field are performed on this personalized model. The model includes implanted electrodes and nerve fibers. We present the results for the bipolar stimulation protocol and predict the voltage spread and the locations of nerve excitation.

Functional simulation of the cochlea for implant optimization

Cochlear implantation is a surgical technique which aims to restore hearing in people with deep hearing loss. However, outcomes of the surgery still exhibit a large variability between patients. Among the factors that contribute to variability the most important are morphological differences in anatomical structures between patients and incorrect implant placements. In order to address these issues, it would be desirable to have a functional model of the cochlea which incorporates inter-patients variability and simulate electrode placement. To this end, we present a finite element model which captures the interaction between the cochlear partition, modeled as an elastic solid with finite deformation, and the perilymph fluid, modeled as a compressible, viscous fluid. Numerical results show that the membrane responds to changes in the stimulation frequencies.

Automated annotation removal in agar plates

Agar plates are widely used in the biomedical field as a medium in which to artificially grow bacteria, algae or fungi. Agar plates (Petri dishes) are used routinely in microbiology laboratories in order to identify the type of micro-organism responsible for infections. Such diagnoses are based on counting the number and type of bacterial colonies growing in the Petri dish. The count of bacterial colonies is a time consuming task prone to human error, so interest in automated counting systems has increased in the recent years. One of the difficulties of automatizing the counting process is the presence of markers and annotations made in the lower part of the agar plate. Efficient removal of such markers can increase the accuracy of the bacterial counting system. This article introduces a fast method for detection, segmentation and removal of annotations in agar plates that improves the results of existing bacterial colony counting algorithms.

Robust, standardized quantification of pulmonary emphysema in low dose CT exams

RATIONALE AND OBJECTIVES

The aim of this study was to present and evaluate a fully automated system for emphysema quantification on low-dose computed tomographic images. The platform standardizes emphysema measurements against changes in the reconstruction algorithm and slice thickness.

MATERIALS AND METHODS

Emphysema was quantified in 149 patients using a fully automatic, in-house developed software (the Robust Automatic On-Line Pulmonary Helper). The accuracy of the system was evaluated against commercial software, and its reproducibility was assessed using pairs of volume-corrected images taken 1 year apart. Furthermore, to standardize quantifications, the effect of changing the reconstruction parameters was modeled using a nonlinear fit, and the inverse of the model function was then applied to the data. The association between quantifications and pulmonary function testing was also evaluated. The accuracy of the in-house software compared to that of commercial software was measured using Spearman's rank correlation coefficient, the mean difference, and the intrasubject variability. Agreement between the methods was studied using Bland-Altman plots. To assess the reproducibility of the method, intraclass correlation coefficients and Bland-Altman plots were used. The statistical significance of the differences between the standardized data and the reference data (soft-tissue reconstruction algorithm B40f; slice thickness, 1 mm) was assessed using a paired two-sample t test.

RESULTS

The accuracy of the method, measured as intrasubject variability, was 3.86 mL for pulmonary volume, 0.01% for emphysema index, and 0.39 Hounsfield units for mean lung density. Reproducibility, assessed using the intraclass correlation coefficient, was >0.95 for all measurements. The standardization method applied to compensate for variations in the reconstruction algorithm and slice thickness increased the intraclass correlation coefficients from 0.87 to 0.97 and from 0.99 to 1.00, respectively. The correlation of the standardized measurements with pulmonary function testing parameters was similar to that of the reference (for the emphysema index and the obstructive subgroup: forced expiratory volume in 1 second, -0.647% vs -0.615%; forced expiratory volume in 1 second/forced vital capacity, -0.672% vs -0.654%; and diffusing capacity for carbon monoxide adjusted for hemoglobin concentration, -0.438% vs -0.523%).

CONCLUSIONS

The new emphysema quantification method presented in this report is accurate and reproducible and, thanks to its standardization method, robust to changes in the reconstruction parameters.

Evaluation of micro-CT for emphysema assessment in mice: comparison with non-radiological techniques

OBJECTIVES

To define the potential, limitations and synergies of micro-CT and other non-radiological techniques for the quantification of emphysema and related processes in mice, by performing a complete characterization of the elastase-induced emphysema model.

MATERIALS AND METHODS

Ninety A/J mice (45 treated and 45 controls) were studied at different time points using breath-hold gated micro-CT, functional test parameters, RT-PCR for RNA cytokine expression, Luminex technology for cytokine plasma concentration and histomorphometry.

RESULTS

Both histomorphometry and micro-CT imaging reflect rapid initial emphysema progression followed by steady-state development at decreasing rates. Cytokine measurements reveal an acute inflammatory response within the first 24 h that disappears after the first week. Limited systemic effect was observed based on plasma cytokine concentration. Lung compliance decreases during the acute inflammation phase and increases afterwards.

CONCLUSION

Histomorphometry is the most sensitive technique since it detects airspace enlargement before the other methods (1 h after treatment). Micro-CT correlates well with histology (r2 = 0.63) proving appropriate for longitudinal studies. Functional test parameters do not necessarily correlate with the extent of emphysema, as they can be influenced by acute inflammation. Finally, cytokine measurements correlate with the presence of inflammation in histology but not with emphysema.

Airway segmentation and analysis for the study of mouse models of lung disease using micro-CT

Animal models of lung disease are gaining importance in understanding the underlying mechanisms of diseases such as emphysema and lung cancer. Micro-CT allows in vivo imaging of these models, thus permitting the study of the progression of the disease or the effect of therapeutic drugs in longitudinal studies. Automated analysis of micro-CT images can be helpful to understand the physiology of diseased lungs, especially when combined with measurements of respiratory system input impedance. In this work, we present a fast and robust murine airway segmentation and reconstruction algorithm. The algorithm is based on a propagating fast marching wavefront that, as it grows, divides the tree into segments. We devised a number of specific rules to guarantee that the front propagates only inside the airways and to avoid leaking into the parenchyma. The algorithm was tested on normal mice, a mouse model of chronic inflammation and a mouse model of emphysema. A comparison with manual segmentations of two independent observers shows that the specificity and sensitivity values of our method are comparable to the inter-observer variability, and radius measurements of the mainstem bronchi reveal significant differences between healthy and diseased mice. Combining measurements of the automatically segmented airways with the parameters of the constant phase model provides extra information on how disease affects lung function.

Clinical and Biomolecular Ontologies for E-Health

This chapter mainly focuses on biomedical knowledge representation and its use in biomedicine. It first illustrates the existent more relevant bioinformatics resources and why they need to be better integrated. Then it describes what the main problems that machines can encounter in processing the factual biomedical knowledge are, what terminologies, classifications and ontologies are, and why they could help in better organizing and exploiting the bioinformatics resources available online. The authors hope that a concise perspective of the field and a list of selected resources, commented with their scope and usability, may help interested people in quickly understanding the main principles of knowledge representation in biomedicine and its high relevance for modern biomedical research and e-health.

A web-enabled database of human gene expression controlled annotations for gene list functional evaluation

Modern biomolecular high-throughput technologies can produce experimental results made of lists of hundreds of candidate interesting genes in the condition under study. These lists need to be biologically interpreted to achieve a better knowledge of the patho-physiological phenomena involved in the studied conditions. To reach this goal, several functional, structural, and phenotypic annotations are available within heterogeneous and widely distributed databanks. Among them, gene expression information is a useful resource to better understand gene functions. We previously developed GFINDer, a Web server that aggregates genomic annotations sparsely available in numerous databanks accessible via the Internet and allows performing statistical analysis of functional and phenotypic annotations of gene lists. To take full advantage of gene expression information provided by eVOC ontologies, we imported them in the GFINDer system. For this purpose and to keep updated such information in the GFINDer database when new releases of them are available, we designed and implemented specific parsing and updating procedures. Moreover, we developed new GFINDer modules that allow annotating human nucleotide sequences with the imported eVOC controlled information on their expression features in order to explore and statistically analyze them.

Exploration and statistical analysis of human gene expression annotations

Gene expression information is a relevant resource useful to better understand gene functions. To evaluate genomic annotations sparsely available in numerous databanks accessible via Internet, we previously developed GFINDer, a Web server that performs statistical analysis of functional and phenotypic annotations of gene lists. To exploit expression information provided by eVOC ontologies, in GFINDer we integrated new modules that allow annotating and statistically analyzing user-classified human nucleotide sequences with controlled information on their expression features.

Utility of biochemical markers in the follow-up of heart transplant recipients

Endomyocardial biopsy (EMB) is currently the standard method to diagnose acute graft rejection. However, considering the potential complications of this procedure, a noninvasive marker of rejection would be an ideal alternative or at least a helpful adjunct to posttransplant management. We measured myoglobin (Myo), creatine kinase MB mass (CK-MBm), troponin T (cTnT), serum amyloid A (SAA), and C-reactive protein (CRP) in 57 patients (mean age 37.5 years) who underwent orthotopic heart transplantation for end-stage cardiac failure between January and December 2001.Endomyocardial biopsies were performed routinely after surgery and histologically diagnosed rejection was graded according to the criteria of the International Society of Heart and Lung Transplantation. Concomittant with the biopsies, blood samples were drawn from the coronary sinus (central blood samples) and from a peripheral vein (peripheral blood samples) to assay biochemical markers. Among 149 EMB evaluated, 87 were negative (grade 0); 28 showed grade 1a rejection; 26 showed grade 1b; and 8 showed grade > 1b (2 were grade 2, 6 were grade 3a). Grades 0 and 1a were considered to be negative, while grades 1b and >1b were considered positive indicating potential acute graft rejection. cTnT, Myo, CK-MBm, SAA, and CRP levels were measured in 149 central blood samples and 149 peripheral blood samples. Myo and CK-MBm did not show significant changes. cTnT seems to be a potentially useful addition to the EMB results, while SAA and CRP showed variations with respect to EMB grade both in central and peripheral samples.

Left atrial function: bridge to central and hormonal determinants of exercise capacity in patients with chronic heart failure

The stroke volume response to exercise is a critical determinant in meeting peripheral metabolic demands in patients with chronic hear failure. The Left atrium, by its position, is important in coupling right and left ventricles, to left preload reserve and to modulate sympathetic activity. We performed this study to investigate the relationship between exercise capacity and diastolic and systolic left atrium function in patients with chronic heart failure.

METHODS

We considered 128 consecutive patients with severe chronic heart failure (EF < 35%) due to ischemic or idiopathic dilated cardiomyopathy. Cardiac output, right atrial pressure, pulmonary artery pressures and mean pulmonary wedge pressure (A, X, V, Y wedge pressures) were determined during right cardiac catheterization. By Echocardiography evaluation, we measured atrial pressures and volume during early and late left atrial systolic filling and we calculated left atrial chamber stiffness by this equation P = A*eKV1. (P = left atrial pressure; A = elastic constant (mmHg*ml); e = the base of the natural logarithm; V1 = left atrial volume (ml); K = left atrial chamber stiffness constant (ml-1) = ln (V/X)/(maximal--minimal left atrial volumes)). All patients performed cardiopulmonary exercise test with modified Noughton protocol. Plasma norepinephrine and Atrial natriuretic factor levels were determined.

RESULTS

Maximal and minimal left atrial volumes were inversely related to oxygen consumption (r = -.44, p < .001; r = -.61, p < .001). At rest, no differences were found in plasma norepinephrine concentrations (309 +/- 152 pg/ml vs 309 +/- 394 pg/ml; p = ns) and systemic vascular resistance (1706 +/- 435 vs 1771 +/- 524 dynes/cm sec-5; p = ns) in patients with large or normal left atrial volumes. During exercise the chronotropic response increased less in patients with large atrial volumes (56 +/- 13 vs 45 +/- 14; p = .001). The left atrial chamber stiffness constant was inversely related to peak oxygen consumption and exercise time. Patients with different chamber stiffness showed statistical difference in peak VO2 (16 +/- 4 vs 11 +/- 3 ml/kg/min; p = .0001). Left atrial ejection fraction was directly related to peak oxygen consumption (r = 0.55), but the most strongly correlation was with atrial filling fraction (r = .67).

CONCLUSIONS

This study demonstrates a strong relationship between left atrial function and exercise capacity in patients with chronic heart failure.