Project description:Cardiovascular diseases have become one of the most prevalent threats to human health throughout the world. As a noninvasive assistant diagnostic tool, the heart sound detection techniques play an important role in the prediction of cardiovascular diseases. In this paper, the latest development of the computer-aided heart sound detection techniques over the last five years has been reviewed. There are mainly the following aspects: the theories of heart sounds and the relationship between heart sounds and cardiovascular diseases; the key technologies used in the processing and analysis of heart sound signals, including denoising, segmentation, feature extraction and classification; with emphasis, the applications of deep learning algorithm in heart sound processing. In the end, some areas for future research in computer-aided heart sound detection techniques are explored, hoping to provide reference to the prediction of cardiovascular diseases.
Project description:Multimaterials deposition, a distinct advantage in bioprinting, overcomes material's limitation in hydrogel-based bioprinting. Multimaterials are deposited in a build/support configuration to improve the structural integrity of three-dimensional bioprinted construct. A combination of rapid cross-linking hydrogel has been chosen for the build/support setup. The bioprinted construct was further chemically cross-linked to ensure a stable construct after print. This paper also proposes a file segmentation and preparation technique to be used in bioprinting for printing freeform structures.
Project description:Computer-aided detection identifies suspicious findings on mammograms to assist radiologists. Since the Food and Drug Administration approved the technology in 1998, it has been disseminated into practice, but its effect on the accuracy of interpretation is unclear.We determined the association between the use of computer-aided detection at mammography facilities and the performance of screening mammography from 1998 through 2002 at 43 facilities in three states. We had complete data for 222,135 women (a total of 429,345 mammograms), including 2351 women who received a diagnosis of breast cancer within 1 year after screening. We calculated the specificity, sensitivity, and positive predictive value of screening mammography with and without computer-aided detection, as well as the rates of biopsy and breast-cancer detection and the overall accuracy, measured as the area under the receiver-operating-characteristic (ROC) curve.Seven facilities (16%) implemented computer-aided detection during the study period. Diagnostic specificity decreased from 90.2% before implementation to 87.2% after implementation (P<0.001), the positive predictive value decreased from 4.1% to 3.2% (P=0.01), and the rate of biopsy increased by 19.7% (P<0.001). The increase in sensitivity from 80.4% before implementation of computer-aided detection to 84.0% after implementation was not significant (P=0.32). The change in the cancer-detection rate (including invasive breast cancers and ductal carcinomas in situ) was not significant (4.15 cases per 1000 screening mammograms before implementation and 4.20 cases after implementation, P=0.90). Analyses of data from all 43 facilities showed that the use of computer-aided detection was associated with significantly lower overall accuracy than was nonuse (area under the ROC curve, 0.871 vs. 0.919; P=0.005).The use of computer-aided detection is associated with reduced accuracy of interpretation of screening mammograms. The increased rate of biopsy with the use of computer-aided detection is not clearly associated with improved detection of invasive breast cancer.
Project description:Nowadays, catheter-based ablation in patients with post-ischemic ventricular tachycardia (VT) is performed in arrhythmogenic sites identified by electrophysiologists by visual inspection during electroanatomic mapping. This work aims to present the development of machine learning tools aiming at supporting clinicians in the identification of arrhythmogenic sites by exploiting innovative features that belong to different domains. This study included 1584 bipolar electrograms from nine patients affected by post-ischemic VT. Different features were extracted in the time, time scale, frequency, and spatial domains and used to train different supervised classifiers. Classification results showed high performance, revealing robustness across the different classifiers in terms of accuracy, true positive, and false positive rates. The combination of multi-domain features with the ensemble tree is the most effective solution, exhibiting accuracies above 93% in the 10-time 10-fold cross-validation and 84% in the leave-one-subject-out validation. Results confirmed the effectiveness of the proposed features and their potential use in a computer-aided system for the detection of arrhythmogenic sites. This work demonstrates for the first time the usefulness of supervised machine learning for the detection of arrhythmogenic sites in post-ischemic VT patients, thus enabling the development of computer-aided systems to reduce operator dependence and errors, thereby possibly improving clinical outcomes.
Project description:To present a computer-aided detection tool for identifying, quantifying, and evaluating tuberculosis (TB) cavities in the infected lungs from computed tomography (CT) scans.The authors' proposed method is based on a novel shape-based automated detection algorithm on CT scans followed by a fuzzy connectedness (FC) delineation procedure. In order to assess interaction between cavities and airways, the authors first roughly identified air-filled structures (airway, cavities, esophagus, etc.) by thresholding over Hounsfield unit of CT image. Then, airway and cavity structure detection was conducted within the support vector machine classification algorithm. Once airway and cavities were detected automatically, the authors extracted airway tree using a hybrid multiscale approach based on novel affinity relations within the FC framework and segmented cavities using intensity-based FC algorithm. At final step, the authors refined airway structures within the local regions of FC with finer control. Cavity segmentation results were compared to the reference truths provided by expert radiologists and cavity formation was tracked longitudinally from serial CT scans through shape and volume information automatically determined through the authors' proposed system. Morphological evolution of the cavitary TB were analyzed accordingly with this process. Finally, the authors computed the minimum distance between cavity surface and nearby airway structures by using the linear time distance transform algorithm to explore potential role of airways in cavity formation and morphological evolution.The proposed methodology was qualitatively and quantitatively evaluated on pulmonary CT images of rabbits experimentally infected with TB, and multiple markers such as cavity volume, cavity surface area, minimum distance from cavity surface to the nearest bronchial-tree, and longitudinal change of these markers (namely, morphological evolution of cavities) were determined precisely. While accuracy of the authors' cavity detection algorithm was 94.61%, airway detection part of the proposed methodology showed even higher performance by 99.8%. Dice similarity coefficients for cavitary segmentation experiments were found to be approximately 99.0% with respect to the reference truths provided by two expert radiologists (blinded to their evaluations). Moreover, the authors noted that volume derived from the authors' segmentation method was highly correlated with those provided by the expert radiologists (R(2) = 0.99757 and R(2) = 0.99496, p < 0.001, with respect to the observer 1 and observer 2) with an interobserver agreement of 98%. The authors quantitatively confirmed that cavity formation was positioned by the nearby bronchial-tree after exploring the respective spatial positions based on the minimum distance measurement. In terms of efficiency, the core algorithms take less than 2 min on a linux machine with 3.47 GHz CPU and 24 GB memory.The authors presented a fully automatic method for cavitary TB detection, quantification, and evaluation. The performance of every step of the algorithm was qualitatively and quantitatively assessed. With the proposed method, airways and cavities were automatically detected and subsequently delineated in high accuracy with heightened efficiency. Furthermore, not only morphological information of cavities were obtained through the authors' proposed framework, but their spatial relation to airways, and longitudinal analysis was also provided to get further insight on cavity formation in tuberculosis disease. To the authors' best of knowledge, this is the first study in computerized analysis of cavitary tuberculosis from CT scans.
Project description:This is a prospective, multicenter, randomized study to evaluate the clinical performance of a novel CADe device, WISE VISION Endoscopy System, in patients undergoing high-definition white light (HDWL) colonoscopy for screening or surveillance of colorectal Cancer (CRC).
Eligible subjects who meet the study inclusion/exclusion criteria will be randomized in a 1:1 ratio to undergo colonoscopy :
* Experimental: CADe colonoscopy procedure with WISE VISION Endoscopy (CADe Group)
* Control: Standard Colonoscopy without CADe (Standard Colonoscopy Group)
Project description:Recent clinical trials using antibodies with low toxicity and high efficiency have raised expectations for the development of next-generation protein therapeutics. However, the process of obtaining therapeutic antibodies remains time consuming and empirical. This review summarizes recent progresses in the field of computer-aided antibody development mainly focusing on antibody modeling, which is divided essentially into two parts: (i) modeling the antigen-binding site, also called the complementarity determining regions (CDRs), and (ii) predicting the relative orientations of the variable heavy (V(H)) and light (V(L)) chains. Among the six CDR loops, the greatest challenge is predicting the conformation of CDR-H3, which is the most important in antigen recognition. Further computational methods could be used in drug development based on crystal structures or homology models, including antibody-antigen dockings and energy calculations with approximate potential functions. These methods should guide experimental studies to improve the affinities and physicochemical properties of antibodies. Finally, several successful examples of in silico structure-based antibody designs are reviewed. We also briefly review structure-based antigen or immunogen design, with application to rational vaccine development.
Project description:After the US Food and Drug Administration (FDA) approved computer-aided detection (CAD) for mammography in 1998, and the Centers for Medicare and Medicaid Services (CMS) provided increased payment in 2002, CAD technology disseminated rapidly. Despite sparse evidence that CAD improves accuracy of mammographic interpretations and costs over $400 million a year, CAD is currently used for most screening mammograms in the United States.To measure performance of digital screening mammography with and without CAD in US community practice.We compared the accuracy of digital screening mammography interpreted with (n = 495 818) vs without (n = 129 807) CAD from 2003 through 2009 in 323 973 women. Mammograms were interpreted by 271 radiologists from 66 facilities in the Breast Cancer Surveillance Consortium. Linkage with tumor registries identified 3159 breast cancers in 323 973 women within 1 year of the screening.Mammography performance (sensitivity, specificity, and screen-detected and interval cancers per 1000 women) was modeled using logistic regression with radiologist-specific random effects to account for correlation among examinations interpreted by the same radiologist, adjusting for patient age, race/ethnicity, time since prior mammogram, examination year, and registry. Conditional logistic regression was used to compare performance among 107 radiologists who interpreted mammograms both with and without CAD.Screening performance was not improved with CAD on any metric assessed. Mammography sensitivity was 85.3% (95% CI, 83.6%-86.9%) with and 87.3% (95% CI, 84.5%-89.7%) without CAD. Specificity was 91.6% (95% CI, 91.0%-92.2%) with and 91.4% (95% CI, 90.6%-92.0%) without CAD. There was no difference in cancer detection rate (4.1 in 1000 women screened with and without CAD). Computer-aided detection did not improve intraradiologist performance. Sensitivity was significantly decreased for mammograms interpreted with vs without CAD in the subset of radiologists who interpreted both with and without CAD (odds ratio, 0.53; 95% CI, 0.29-0.97).Computer-aided detection does not improve diagnostic accuracy of mammography. These results suggest that insurers pay more for CAD with no established benefit to women.
Project description:BackgroundWith the growth of machine learning applications, the practice of medicine is evolving. Computer-aided detection (CAD) is a software technology that has become widespread in radiology practices, particularly in breast cancer screening for improving detection rates at earlier stages. Many studies have investigated the diagnostic accuracy of CAD, but its implementation in clinical settings has been largely overlooked.ObjectiveThe aim of this scoping review was to summarize recent literature on the adoption and implementation of CAD during breast cancer screening by radiologists and to describe barriers and facilitators for CAD use.MethodsThe MEDLINE database was searched for English, peer-reviewed articles that described CAD implementation, including barriers or facilitators, in breast cancer screening and were published between January 2010 and March 2018. Articles describing the diagnostic accuracy of CAD for breast cancer detection were excluded. The search returned 526 citations, which were reviewed in duplicate through abstract and full-text screening. Reference lists and cited references in the included studies were reviewed.ResultsA total of nine articles met the inclusion criteria. The included articles showed that there is a tradeoff between the facilitators and barriers for CAD use. Facilitators for CAD use were improved breast cancer detection rates, increased profitability of breast imaging, and time saved by replacing double reading. Identified barriers were less favorable perceptions of CAD compared to double reading by radiologists, an increase in recall rates of patients for further testing, increased costs, and unclear effect on patient outcomes.ConclusionsThere is a gap in the literature between CAD's well-established diagnostic accuracy and its implementation and use by radiologists. Generally, the perceptions of radiologists have not been considered and details of implementation approaches for adoption of CAD have not been reported. The cost-effectiveness of CAD has not been well established for breast cancer screening in various populations. Further research is needed on how to best facilitate CAD in radiology practices in order to optimize patient outcomes, and the views of radiologists need to be better considered when advancing CAD use.
Project description:Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities ∼ 100% of but at high FP levels. By leveraging existing CADe systems, coordinates of regions or volumes of interest (ROI or VOI) are generated and function as input for a second tier, which is our focus in this study. In this second stage, we generate 2D (two-dimensional) or 2.5D views via sampling through scale transformations, random translations and rotations. These random views are used to train deep convolutional neural network (ConvNet) classifiers. In testing, the ConvNets assign class (e.g., lesion, pathology) probabilities for a new set of random views that are then averaged to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. The methods are evaluated on three data sets: 59 patients for sclerotic metastasis detection, 176 patients for lymph node detection, and 1,186 patients for colonic polyp detection. Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets. Our proposed methods improve performance markedly in all cases. Sensitivities improved from 57% to 70%, 43% to 77%, and 58% to 75% at 3 FPs per patient for sclerotic metastases, lymph nodes and colonic polyps, respectively.