scholarly journals Artificial Intelligence-Based Colorectal Polyp Histology Prediction: High Accuracy in Larger Polyps

2022 ◽  
Author(s):  
Naoki Muguruma ◽  
Tetsuji Takayama
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Polyp ◽  
High Accuracy

Su1716 ARTIFICIAL INTELLIGENCE FOR COLORECTAL POLYP DETECTION: HIGH ACCURACY AND DETECTION ANTICIPATION WITH CB-17-08 PERFORMANCE

2019 ◽  
Vol 89 (6) ◽  
pp. AB391-AB392 ◽  
Author(s):  
Alessandro Repici ◽  
Nhan Ngo Dinh ◽  
Andrea Cherubini ◽  
Roberta Maselli ◽  
Piera Alessia Galtieri ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Polyp ◽  
High Accuracy ◽  
Polyp Detection

scholarly journals Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Abdulmalek Ahmed ◽  
Salaheldin Elkatatny ◽  
Abdulwahab Ali ◽  
Mahmoud Abughaban ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Drilling Fluid ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Functional Networks ◽  
Lost Circulation ◽  
Drilling Parameters ◽  
High Pressure High Temperature ◽  
Drilling Cost

Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field. The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones. Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them. High accuracy was achieved by the three AI models based on the root mean square error (RMSE), confusion matrix, and correlation coefficient (R). All the AI models identified the lost circulation zones in Well A with high accuracy where the R is more than 0.98 and RMSE is less than 0.09. ANN is the most accurate model with R=0.99 and RMSE=0.05. An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with R=0.946 and RMSE=0.165 and R=0.952 and RMSE=0.155, respectively.

scholarly journals Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series

Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 155
Author(s):  
Joaquim Carreras ◽  
Naoya Nakamura ◽  
Rifat Hamoudi
Keyword(s):  
Artificial Intelligence ◽  
Overall Survival ◽  
B Cell ◽  
Mantle Cell Lymphoma ◽  
Cell Lymphoma ◽  
High Accuracy ◽  
Gene Set Enrichment Analysis ◽  
Intelligence Analysis ◽  
Mantle Cell ◽  
Pan Cancer

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.

scholarly journals Pattern Recognition using a Neural Network on a Microcontroller with I2C Ultrasonic Sensors

2019 ◽  
Vol 3 (1) ◽  
pp. 9-19 ◽  
Author(s):  
Fazal Noor
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Integrated Circuit ◽  
High Accuracy ◽  
Ultrasonic Sensors ◽  
Motion Patterns ◽  
The Neural Network ◽  
Proposed Model ◽  
Neural Network Algorithm

Ultrasonic sensors have been used in a variety of applications to measure ranges to objects. Hand gestures via ultrasonic sensors form unique motion patterns for controls. In this research, patterns formed by placing a set of objects in a grid of cells are used for control purposes. A neural network algorithm is implemented on a microcontroller which takes in range signals as inputs read from ultrasonic sensors and classifies them in one of four classes. The neural network is then trained to classify patterns based on objects’ locations in real-time. The testing of the neural network for pattern recognition is performed on a testbed consisting of Inter-Integrated Circuit (I2C) ultrasonic sensors and a microcontroller. The performance of the proposed model is presented and it is observed the model is highly scalable, accurate, robust and reliable for applications requiring high accuracy such as in robotics and artificial intelligence.

scholarly journals MIXTURE of human expertise and deep learning - Developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease

2021 ◽  
Author(s):  
Wataru Uegami ◽  
Andrey Bychkov ◽  
Mutsumi Ozasa ◽  
Kazuki Uehara ◽  
Kensuke Kataoka ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Interstitial Pneumonia ◽  
Proportional Hazards Model ◽  
Histopathological Examination ◽  
Usual Interstitial Pneumonia ◽  
High Accuracy ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Learning Models

Interstitial pneumonia is a heterogeneous disease with a progressive course and poor prognosis, at times even worse than those in the main cancer types. Histopathological examination is crucial for its diagnosis and estimation of prognosis. However, the evaluation strongly depends on the experience of pathologists, and the reproducibility of diagnosis is low. Herein, we propose MIXTURE (huMan-In-the-loop eXplainable artificial intelligence Through the Use of REcurrent training), a method to develop deep learning models for extracting pathologically significant findings based on an expert pathologist's perspective with a small annotation effort. The procedure of MIXTURE consists of three steps as follows. First, we created feature extractors for tiles from whole slide images using self-supervised learning. The similar looking tiles were clustered based on the output features and then pathologists integrated the pathologically synonymous clusters. Using the integrated clusters as labeled data, deep learning models to classify the tiles into pathological findings were created by transfer-learning the feature extractors. We developed three models for different magnifications. Using these extracted findings, our model was able to predict the diagnosis of usual interstitial pneumonia, a finding suggestive of progressive disease, with high accuracy (AUC 0.90). This high accuracy could not be achieved without the integration of findings by pathologists. The patients predicted as UIP had significantly poorer prognosis (five-year overall survival [OS]: 55.4% than those predicted as non-UIP (OS: 95.2%). The Cox proportional hazards model for each microscopic finding and prognosis pointed out dense fibrosis, fibroblastic foci, elastosis, and lymphocyte aggregation as independent risk factors. We suggest that MIXTURE may serve as a model approach to different diseases evaluated by medical imaging, including pathology and radiology, and be the prototype for artificial intelligence that can collaborate with humans.

scholarly journals Computational analysis of transition temperatures (Tts) of proteins fused to elastin-like polypeptide (ELP): deep fake evaluation of proteins, linkers, and trailers features

2021 ◽  
Author(s):  
Mohammad Davoud Ghafari ◽  
Iraj Rasooli ◽  
Khosro Khajeh ◽  
Bahareh Dabirmanesh ◽  
Mohammadreza Ghafari ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Phase Transition ◽  
Transition Temperature ◽  
Computational Analysis ◽  
Large Data ◽  
High Accuracy ◽  
Sequence Length ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Protein Feature

The phase transition temperature (Tt) prediction of the Elastin-like polypeptides (ELPs) is not trivial because it is related to complex sets of variables such as composition, sequence length, hydrophobic characterization, hydrophilic characterization, the sequence order in the fused proteins, linkers and trailer constructs. In this paper, two unique quantitative models are presented for the prediction of the Tt of a family of ELPs that could be fused to different proteins, linkers, and trailers. The lack of need to use multiple software, peptide information, such as PDB file, as well as knowing the second and third structures of proteins are the advantages of this model besides its high accuracy and speed. One of our models could predict the Tt values of the fused ELPs by entering the protein, linker, and trailer features with R2=99%. Also, another model is able to predict the Tt value by entering the fused protein feature with R2=96%. For more reliability, our method is enriched by Artificial Intelligence (AI) to generate similar proteins. In this regard, Generative Adversarial Network (GAN) is our AI method to create fake proteins and similar values. The experimental results show that our strategy for prediction of Tt is reliable in large data.

scholarly journals Automated diagnosis of atrial fibrillation in 24-hour Holter recording based on deep learning:a study with randomized and real-world data validation

2021 ◽  
Author(s):  
Peng Zhang ◽  
Fan Lin ◽  
Fei Ma ◽  
Yuting Chen ◽  
Daowen Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Deep Learning ◽  
Real World ◽  
High Accuracy ◽  
Patient Identification ◽  
Real World Data ◽  
Clinical Dataset ◽  
Test Sets

SummaryBackgroundWith the increasing demand for atrial fibrillation (AF) screening, clinicians spend a significant amount of time in identifying the AF signals from massive electrocardiogram (ECG) data in long-term dynamic ECG monitoring. In this study, we aim to reduce clinicians’ workload and promote AF screening by using artificial intelligence (AI) to automatically detect AF episodes and identify AF patients in 24 h Holter recording.MethodsWe used a total of 22 979 Holter recordings (24 h) from 22 757 adult patients and established accurate annotations for AF by cardiologists. First, a randomized clinical cohort of 3 000 recordings (1 500 AF and 1 500 non-AF) from 3000 patients recorded between April 2012 and May 2020 was collected and randomly divided into training, validation and test sets (10:1:4). Then, a deep-learning-based AI model was developed to automatically detect AF episode using RR intervals and was tested with the test set. Based on AF episode detection results, AF patients were automatically identified by using a criterion of at least one AF episode of 6 min or longer. Finally, the clinical effectiveness of the model was verified with an independent real-world test set including 19 979 recordings (1 006 AF and 18 973 non-AF) from 19 757 consecutive patients recorded between June 2020 and January 2021.FindingsOur model achieved high performance for AF episode detection in both test sets (sensitivity: 0.992 and 0.972; specificity: 0.997 and 0.997, respectively). It also achieved high performance for AF patient identification in both test sets (sensitivity:0.993 and 0.994; specificity: 0.990 and 0.973, respectively). Moreover, it obtained superior and consistent performance in an external public database.InterpretationOur AI model can automatically identify AF in long-term ECG recording with high accuracy. This cost-effective strategy may promote AF screening by improving diagnostic effectiveness and reducing clinical workload.Research in contextEvidence before this studyWe searched Google Scholar and PubMed for research articles on artificial intelligence-based diagnosis of atrial fibrillation (AF) published in English between Jan 1, 2016 and Aug 1, 2021, using the search terms “deep learning” OR “deep neural network” OR “machine learning” OR “artificial intelligence” AND “atrial fibrillation”. We found that most of the previous deep learning models in AF detection were trained and validated on benchmark datasets (such as the PhysioNet database, the Massachusetts Institute of Technology Beth Israel Hospital AF database or Long-Term AF database), in which there were less than 100 patients or the recordings contained only short ECG segments (30-60s). Our search did not identify any articles that explored deep neural networks for AF detection in large real-world dataset of 24 h Holter recording, nor did we find articles that can automatically identify patients with AF in 24 h Holter recording.Added value of this studyFirst, long-term Holter monitoring is the main method of AF screening, however, most previous studies of automatic AF detection mainly tested on short ECG recordings. This work focused on 24 h Holter recording data and achieved high accuracy in detecting AF episodes. Second, AF episodes detection did not automatically transform to AF patient identification in 24 h Holter recording, since at present, there is no well-recognized criterion for automatically identifying AF patient. Therefore, we established a criterion to identify AF patients by use of at least one AF episode of 6 min or longer, as this condition led to significantly increased risk of thromboembolism. Using this criterion, our method identified AF patients with high accuracy. Finally, and more importantly, our model was trained on a randomized clinical dataset and tested on an independent real-world clinical dataset to show great potential in clinical application. We did not exclude rare or special cases in the real-world dataset so as not to inflate our AF detection performance. To the best of our knowledge, this is the first study to automatically identifies both AF episodes and AF patients in 24 h Holter recording of large real-world clinical dataset.Implications of all the available evidenceOur deep learning model automatically identified AF patient with high accuracy in 24 h Holter recording and was verified in real-world data, therefore, it can be embedded into the Holter analysis system and deployed at the clinical level to assist the decision making of Holter analysis system and clinicians. This approach can help improve the efficiency of AF screening and reduce the cost for AF diagnosis. In addition, our RR-interval-based model achieved comparable or better performance than the raw-ECG-based method, and can be widely applied to medical devices that can collect heartbeat information, including not only the multi-lead and single-lead Holter devices, but also other wearable devices that can reliably measure the heartbeat signals.

Sign in / Sign up

Export Citation Format

Share Document