scholarly journals Artificial Intelligence assessment of Renal Scarring (AIRS study)

Kidney360 ◽  
2021 ◽  
pp. 10.34067/KID.0003662021
Author(s):  
Chanon Chantaduly ◽  
Hayden R. Troutt ◽  
Karla A. Perez Reyes ◽  
Jonathan E. Zuckerman ◽  
Peter D. Chang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Kidney Biopsy ◽  
Interstitial Fibrosis ◽  
Renal Scarring ◽  
Machine Learning Algorithms ◽  
Ct Scans ◽  
Tubular Atrophy ◽  
Kidney Fibrosis ◽  
Non Invasive

Background: The goal of the Artificial Intelligence in Renal Scarring (AIRS) study is to develop machine learning tools for non-invasive quantification of kidney fibrosis from imaging scans. Methods: We conducted a retrospective analysis of patients who had one or more abdominal computed tomography (CT) scans within 6 months of a kidney biopsy. The final cohort encompassed 152 CT scans from 92 patients which included images of 300 native kidneys and 76 transplant kidneys. Two different convolutional neural networks (slice-level and voxel-level classifiers) were tested to differentiate severe vs mild/moderate kidney fibrosis (≥50% vs <50%). Interstitial fibrosis and tubular atrophy scores from kidney biopsy reports were used as ground-truth. Results: The two machine learning models demonstrated similar positive predictive value (0.886 vs 0.935) and accuracy (0.831 vs 0.879). Conclusions: In summary, machine learning algorithms are a promising non-invasive diagnostic tool to quantify kidney fibrosis from CT scans. The clinical utility of these prediction tools, in terms of avoiding renal biopsy and associated bleeding risks in patients with severe fibrosis, remains to be validated in prospective clinical trials.

scholarly journals Proteomic Characterization of Urinary Extracellular Vesicles from Kidney-Transplanted Patients Treated with Calcineurin Inhibitors

2020 ◽  
Vol 21 (20) ◽  
pp. 7569
Author(s):  
Laura Carreras-Planella ◽  
Javier Juega ◽  
Omar Taco ◽  
Laura Cañas ◽  
Marcella Franquesa ◽  
...  
Keyword(s):  
Extracellular Vesicles ◽  
Interstitial Fibrosis ◽  
Immunosuppressive Agents ◽  
Calcineurin Inhibitors ◽  
Immunosuppressive Drugs ◽  
Protein Biomarkers ◽  
Tubular Atrophy ◽  
Kidney Fibrosis ◽  
Non Invasive ◽  
Specific Effects

Use of immunosuppressive drugs is still unavoidable in kidney-transplanted patients. Since their discovery, calcineurin inhibitors (CNI) have been considered the first-line immunosuppressive agents, in spite of their known nephrotoxicity. Chronic CNI toxicity (CNIT) may lead to kidney fibrosis, a threatening scenario for graft survival. However, there is still controversy regarding CNIT diagnosis, monitoring and therapeutic management, and their specific effects at the molecular level are not fully known. Aiming to better characterize CNIT patients, in the present study, we collected urine from kidney-transplanted patients treated with CNI who (i) had a normal kidney function, (ii) suffered CNIT, or (iii) presented interstitial fibrosis and tubular atrophy (IFTA). Urinary extracellular vesicles (uEV) were enriched and the proteome was analyzed to get insight into changes happening during CNI. Members of the uroplakin and plakin families were significantly upregulated in the CNIT group, suggesting an important role in CNIT processes. Although biomarkers cannot be asserted from this single pilot study, our results evidence the potential of uEV as a source of non-invasive protein biomarkers for a better detection and monitoring of this renal alteration in kidney-transplanted patients.

scholarly journals A Novel Urinary Proteomics Classifier for Non-Invasive Evaluation of Interstitial Fibrosis and Tubular Atrophy in Chronic Kidney Disease

Proteomes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 32
Author(s):  
Lorenzo Catanese ◽  
Justyna Siwy ◽  
Emmanouil Mavrogeorgis ◽  
Kerstin Amann ◽  
Harald Mischak ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Chronic Kidney Disease ◽  
Renal Fibrosis ◽  
Kidney Biopsy ◽  
Interstitial Fibrosis ◽  
Peptide Fragments ◽  
Tubular Atrophy ◽  
Non Invasive ◽  
Urinary Peptide ◽  
Urinary Peptides

Non-invasive urinary peptide biomarkers are able to detect and predict chronic kidney disease (CKD). Moreover, specific urinary peptides enable discrimination of different CKD etiologies and offer an interesting alternative to invasive kidney biopsy, which cannot always be performed. The aim of this study was to define a urinary peptide classifier using mass spectrometry technology to predict the degree of renal interstitial fibrosis and tubular atrophy (IFTA) in CKD patients. The urinary peptide profiles of 435 patients enrolled in this study were analyzed using capillary electrophoresis coupled with mass spectrometry (CE-MS). Urine samples were collected on the day of the diagnostic kidney biopsy. The proteomics data were divided into a training (n = 200) and a test (n = 235) cohort. The fibrosis group was defined as IFTA ≥ 15% and no fibrosis as IFTA < 10%. Statistical comparison of the mass spectrometry data enabled identification of 29 urinary peptides with differential occurrence in samples with and without fibrosis. Several collagen fragments and peptide fragments of fetuin-A and others were combined into a peptidomic classifier. The classifier separated fibrosis from non-fibrosis patients in an independent test set (n = 186) with area under the curve (AUC) of 0.84 (95% CI: 0.779 to 0.889). A significant correlation of IFTA and FPP_BH29 scores could be observed Rho = 0.5, p < 0.0001. We identified a peptidomic classifier for renal fibrosis containing 29 peptide fragments corresponding to 13 different proteins. Urinary proteomics analysis can serve as a non-invasive tool to evaluate the degree of renal fibrosis, in contrast to kidney biopsy, which allows repeated measurements during the disease course.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Artificial Intelligence, Machine Learning and Calculation of Intraocular Lens Power

2020 ◽  
Vol 237 (12) ◽  
pp. 1430-1437
Author(s):  
Achim Langenbucher ◽  
Nóra Szentmáry ◽  
Jascha Wendelstein ◽  
Peter Hoffmann
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Regression Model ◽  
Prediction Error ◽  
Learning Algorithms ◽  
Anterior Chamber Depth ◽  
Gaussian Process Regression ◽  
Machine Learning Algorithms ◽  
Effect Sizes ◽  
Axial Position

Abstract Background and Purpose In the last decade, artificial intelligence and machine learning algorithms have been more and more established for the screening and detection of diseases and pathologies, as well as for describing interactions between measures where classical methods are too complex or fail. The purpose of this paper is to model the measured postoperative position of an intraocular lens implant after cataract surgery, based on preoperatively assessed biometric effect sizes using techniques of machine learning. Patients and Methods In this study, we enrolled 249 eyes of patients who underwent elective cataract surgery at Augenklinik Castrop-Rauxel. Eyes were measured preoperatively with the IOLMaster 700 (Carl Zeiss Meditec), as well as preoperatively and postoperatively with the Casia 2 OCT (Tomey). Based on preoperative effect sizes axial length, corneal thickness, internal anterior chamber depth, thickness of the crystalline lens, mean corneal radius and corneal diameter a selection of 17 machine learning algorithms were tested for prediction performance for calculation of internal anterior chamber depth (AQD_post) and axial position of equatorial plane of the lens in the pseudophakic eye (LEQ_post). Results The 17 machine learning algorithms (out of 4 families) varied in root mean squared/mean absolute prediction error between 0.187/0.139 mm and 0.255/0.204 mm (AQD_post) and 0.183/0.135 mm and 0.253/0.206 mm (LEQ_post), using 5-fold cross validation techniques. The Gaussian Process Regression Model using an exponential kernel showed the best performance in terms of root mean squared error for prediction of AQDpost and LEQpost. If the entire dataset is used (without splitting for training and validation data), comparison of a simple multivariate linear regression model vs. the algorithm with the best performance showed a root mean squared prediction error for AQD_post/LEQ_post with 0.188/0.187 mm vs. the best performance Gaussian Process Regression Model with 0.166/0.159 mm. Conclusion In this paper we wanted to show the principles of supervised machine learning applied to prediction of the measured physical postoperative axial position of the intraocular lenses. Based on our limited data pool and the algorithms used in our setting, the benefit of machine learning algorithms seems to be limited compared to a standard multivariate regression model.

scholarly journals mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Artur Yakimovich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Edge Label ◽  
Bacterial Colony ◽  
Link Type ◽  
Colony Counting ◽  
Anthrax Spores ◽  
Infection Biology

ABSTRACT Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSphere of Influence article, he reflects on two papers “Holographic Deep Learning for Rapid Optical Screening of Anthrax Spores” by Jo et al. (Y. Jo, S. Park, J. Jung, J. Yoon, et al., Sci Adv 3:e1700606, 2017, https://doi.org/10.1126/sciadv.1700606) and “Bacterial Colony Counting with Convolutional Neural Networks in Digital Microbiology Imaging” by Ferrari and colleagues (A. Ferrari, S. Lombardi, and A. Signoroni, Pattern Recognition 61:629–640, 2017, https://doi.org/10.1016/j.patcog.2016.07.016). Here he discusses how these papers made an impact on him by showcasing that artificial intelligence algorithms can be equally applicable to both classical infection biology techniques and cutting-edge label-free imaging of pathogens.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

2021 ◽  
Vol 18 (11) ◽  
pp. 6099
Author(s):  
Joel Weijia Lai ◽  
Candice Ke En Ang ◽  
U. Rajendra Acharya ◽  
Kang Hao Cheong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Daily Living ◽  
Learning Algorithms ◽  
Health Condition ◽  
Machine Learning Algorithms ◽  
Human Cognition ◽  
Computer Algorithms ◽  
Large Sets ◽  
Prevention Methods

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.

scholarly journals Cellular Mechanisms of Tissue Fibrosis. 3. Novel mechanisms of kidney fibrosis

2013 ◽  
Vol 304 (7) ◽  
pp. C591-C603 ◽  
Author(s):  
Gabriela Campanholle ◽  
Giovanni Ligresti ◽  
Sina A. Gharib ◽  
Jeremy S. Duffield
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Interstitial Fibrosis ◽  
Kidney Injury ◽  
Innate Immune ◽  
Cellular Mechanisms ◽  
Tubular Atrophy ◽  
Kidney Fibrosis ◽  
Capillary Rarefaction ◽  
Peritubular Capillaries

Chronic kidney disease, defined as loss of kidney function for more than three months, is characterized pathologically by glomerulosclerosis, interstitial fibrosis, tubular atrophy, peritubular capillary rarefaction, and inflammation. Recent studies have identified a previously poorly appreciated, yet extensive population of mesenchymal cells, called either pericytes when attached to peritubular capillaries or resident fibroblasts when embedded in matrix, as the progenitors of scar-forming cells known as myofibroblasts. In response to sustained kidney injury, pericytes detach from the vasculature and differentiate into myofibroblasts, a process not only causing fibrosis, but also directly contributing to capillary rarefaction and inflammation. The interrelationship of these three detrimental processes makes myofibroblasts and their pericyte progenitors an attractive target in chronic kidney disease. In this review, we describe current understanding of the mechanisms of pericyte-to-myofibroblast differentiation during chronic kidney disease, draw parallels with disease processes in the glomerulus, and highlight promising new therapeutic strategies that target pericytes or myofibroblasts. In addition, we describe the critical paracrine roles of epithelial, endothelial, and innate immune cells in the fibrogenic process.

AI-Enabled Support System for Melanoma Detection and Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 58-75
Author(s):  
Vivek Sen Saxena ◽  
Prashant Johri ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Lesion ◽  
Performance Metrics ◽  
Similarity Index ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Lesion Area ◽  
Melanoma Detection ◽  
Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

Abstract 17060: Machine Learning and Video Recognition for Automated Detection of Fluid Status in Heart Failure Patients

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Pratik Doshi ◽  
John Tanaka ◽  
Jedrek Wosik ◽  
Natalia M Gil ◽  
Martin Bertran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Point Of Care ◽  
Machine Learning Algorithms ◽  
Right Atrial ◽  
Heart Catheterization ◽  
Heart Failure Patients ◽  
Non Invasive ◽  
Fluid Status ◽  
Video Recognition

Introduction: There is a need for innovative solutions to better screen and diagnose the 7 million patients with chronic heart failure. A key component of assessing these patients is monitoring fluid status by evaluating for the presence and height of jugular venous distension (JVD). We hypothesize that video analysis of a patient’s neck using machine learning algorithms and image recognition can identify the amount of JVD. We propose the use of high fidelity video recordings taken using a mobile device camera to determine the presence or absence of JVD, which we will use to develop a point of care testing tool for early detection of acute exacerbation of heart failure. Methods: In this feasibility study, patients in the Duke cardiac catheterization lab undergoing right heart catheterization were enrolled. RGB and infrared videos were captured of the patient’s neck to detect JVD and correlated with right atrial pressure on the heart catheterization. We designed an adaptive filter based on biological priors that enhances spatially consistent frequency anomalies and detects jugular vein distention, with implementation done on Python. Results: We captured and analyzed footage for six patients using our model. Four of these six patients shared a similar strong signal outliner within the frequency band of 95bpm – 200bpm when using a conservative threshold, indicating the presence of JVD. We did not use statistical analysis given the small nature of our cohort, but in those we detected a positive JVD signal the RA mean was 20.25 mmHg and PCWP mean was 24.3 mmHg. Conclusions: We have demonstrated the ability to evaluate for JVD via infrared video and found a relationship with RHC values. Our project is innovative because it uses video recognition and allows for novel patient interactions using a non-invasive screening technique for heart failure. This tool can become a non-invasive standard to both screen for and help manage heart failure patients.

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