Artificial intelligence in luminal endoscopy

Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett’s, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice.

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Deep Learning Model Improves Radiologists’ Performance in Detection and Classification of Breast Lesions

10.21203/rs.3.rs-746374/v1 ◽

2021 ◽

Author(s):

Ying-Shi Sun ◽

Yu-Hong Qu ◽

Dong Wang ◽

Yi Li ◽

Lin Ye ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Roc Curve ◽

False Positive ◽

Large Scale ◽

False Positive Rate ◽

Training Dataset ◽

Validation Dataset ◽

Breast Lesions ◽

Positive Rate

Abstract Background: Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application.Methods: This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, retrospectively collected mammograms from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists’ performance with and without it. Finally, prospectively multicenter mammograms were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve.Results: The sensitivity of model for detecting lesion after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign from malignant lesions was 0.855 (95% CI: 0.830, 0.880). The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852 vs. 0.808, P = 0.005). The mean reading time of with the model was shorter than that of reading alone (80.18 s vs. 62.28 s, P = 0.03). In prospective application, the sensitivity of detection reached 0.887 at false positive rate of 0.25; the AUC of radiologists with the model was 0.983 (95% CI: 0.978, 0.988), with sensitivity, specificity, PPV, and NPV of 94.36%, 98.07%, 87.76%, and 99.09%, respectively.Conclusions: The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.Trial registration: NCT, NCT03708978. Registered 17 April 2018, https://register.clinicaltrials.gov/prs/app/ NCT03708978

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A Study on Deep Learning Methods in the Concept of Digital Industry 4.0

Handbook of Research on Management and Strategies for Digital Enterprise Transformation - Advances in E-Business Research ◽

10.4018/978-1-7998-5015-1.ch016 ◽

2021 ◽

pp. 318-339

Author(s):

Mehmet Ali Şimşek ◽

Zeynep Orman

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Deep Learning ◽

Industry 4.0 ◽

Production Efficiency ◽

Learning Technologies ◽

High Potential ◽

Data Sets ◽

Learning Methods ◽

Intelligence Studies

Nowadays, the main features of Industry 4.0 are interpreted to the ability of machines to communicate with each other and with a system, increasing the production efficiency and development of the decision-making mechanisms of robots. In these cases, new analytical algorithms of Industry 4.0 are needed. By using deep learning technologies, various industrial challenging problems in Industry 4.0 can be solved. Deep learning provides algorithms that can give better results on datasets owing to hidden layers. In this chapter, deep learning methods used in Industry 4.0 are examined and explained. In addition, data sets, metrics, methods, and tools used in the previous studies are explained. This study can lead to artificial intelligence studies with high potential to accelerate the implementation of Industry 4.0. Therefore, the authors believe that it will be very useful for researchers and practitioners who want to do research on this topic.

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Selective Disinfection Based on Directional Ultraviolet Irradiation and Artificial Intelligence

Electronics ◽

10.3390/electronics10202557 ◽

2021 ◽

Vol 10 (20) ◽

pp. 2557

Author(s):

Ben Zierdt ◽

Taichu Shi ◽

Thomas DeGroat ◽

Sam Furman ◽

Nicholas Papas ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Ultraviolet Radiation ◽

Ultraviolet Irradiation ◽

Human Exposure ◽

Large Scale ◽

Learning Algorithm ◽

Ultraviolet Disinfection ◽

Time Efficiency ◽

Deep Learning Algorithm

Ultraviolet disinfection has been proven to be effective for surface sanitation. Traditional ultraviolet disinfection systems generate omnidirectional radiation, which introduces safety concerns regarding human exposure. Large scale disinfection must be performed without humans present, which limits the time efficiency of disinfection. We propose and experimentally demonstrate a targeted ultraviolet disinfection system using a combination of robotics, lasers, and deep learning. The system uses a laser-galvo and a camera mounted on a two-axis gimbal running a custom deep learning algorithm. This allows ultraviolet radiation to be applied to any surface in the room where it is mounted, and the algorithm ensures that the laser targets the desired surfaces avoids others such as humans. Both the laser-galvo and the deep learning algorithm were tested for targeted disinfection.

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Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis

Endoscopy International Open ◽

10.1055/a-1341-0457 ◽

2021 ◽

Vol 09 (04) ◽

pp. E513-E521

Author(s):

Munish Ashat ◽

Jagpal Singh Klair ◽

Dhruv Singh ◽

Arvind Rangarajan Murali ◽

Rajesh Krishnamoorthi

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Meta Analysis ◽

Screening Colonoscopy ◽

Average Risk ◽

Polyp Detection ◽

Withdrawal Time ◽

Detection Rates ◽

Adenoma Detection ◽

The Impact

Abstract Background and study aims With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice. Methods We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT). Results Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55–2.00; I2 = 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68–2.15, I2 = 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00–0.92) minutes, I2 = 94 %). Conclusions There is an increase in adenoma and polyp detection with the utilization of AIAC.

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PAI-WSIT: a Comprehensive Curated Resource for Cancerous Pathology With Deep Learning

10.21203/rs.3.rs-495066/v1 ◽

2021 ◽

Author(s):

Changjiang Zhou ◽

Xiaobing Feng ◽

Yi Jin ◽

Harvest F. Gu ◽

Youcai Zhao ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Data Collection ◽

Function Analysis ◽

Digital Pathology ◽

Clinical Information ◽

Improve Patient Care ◽

The Cancer Genome Atlas ◽

Precision Oncology ◽

Region Detection

Abstract BackgroundThe possibility of digitizing whole-slide images (WSI) of tissue has led to the advent of artificial intelligence (AI) in digital pathology. Advances in precision oncology have resulted in an increasing demand for predictive assays that enable mining of subvisual morphometric phenotypes and might improve patient care ultimately. Hence, a pathologist-annotated and artificial intelligence-empowered platform for integration and analysis of WSI data and molecular detection data in tumors was established, called PAI-WSIT (http://www.paiwsit.com).MethodsThe standardized data collection process was used for data collection in PAI-WSIT, while a multifunctional annotation tool was developed and a user-friendly search engine and web interface were integrated for the database access. Furthermore, deep learning frameworks were applied in two tasks to detect malignant regions and classify phenotypic subtypes in colorectal cancers (CRCs), respectively.ResultsPAI-WSIT recorded 8633 WSIs of 1772 tumor cases, of which CRC from four regional hospitals in China and The Cancer Genome Atlas (TCGA) were the main ones, as well as cancers in breast, lung, prostate, bladder, and kidneys from two Chinese hospitals. A total of 1298 WSIs with high-quality annotations were evaluated by a panel of 8 pathologists. Gene detection reports of 582 tumor cases were collected. Clinical information of all tumor cases was documented. Besides, we reached overall accuracy of 0.933 in WSI classification for malignant region detection of CRC, and aera under the curves (AUC) of 0.719 on colorectal subtype dataset.ConclusionsCollectively, the annotation function, data integration and AI function analysis of PAI-WSIT provide support for AI-assisted tumor diagnosis, all of which have provided a comprehensive curation of carcinomas pathology.

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Genomic data imputation with variational auto-encoders

GigaScience ◽

10.1093/gigascience/giaa082 ◽

2020 ◽

Vol 9 (8) ◽

Author(s):

Yeping Lina Qiu ◽

Hong Zheng ◽

Olivier Gevaert

Keyword(s):

Deep Learning ◽

Missing Data ◽

Large Scale ◽

Missing Values ◽

Genomic Data ◽

Large Data ◽

Missing At Random ◽

Data Sets ◽

Data Imputation ◽

Missing Not At Random

Abstract Background As missing values are frequently present in genomic data, practical methods to handle missing data are necessary for downstream analyses that require complete data sets. State-of-the-art imputation techniques, including methods based on singular value decomposition and K-nearest neighbors, can be computationally expensive for large data sets and it is difficult to modify these algorithms to handle certain cases not missing at random. Results In this work, we use a deep-learning framework based on the variational auto-encoder (VAE) for genomic missing value imputation and demonstrate its effectiveness in transcriptome and methylome data analysis. We show that in the vast majority of our testing scenarios, VAE achieves similar or better performances than the most widely used imputation standards, while having a computational advantage at evaluation time. When dealing with data missing not at random (e.g., few values are missing), we develop simple yet effective methodologies to leverage the prior knowledge about missing data. Furthermore, we investigate the effect of varying latent space regularization strength in VAE on the imputation performances and, in this context, show why VAE has a better imputation capacity compared to a regular deterministic auto-encoder. Conclusions We describe a deep learning imputation framework for transcriptome and methylome data using a VAE and show that it can be a preferable alternative to traditional methods for data imputation, especially in the setting of large-scale data and certain missing-not-at-random scenarios.

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Artificial intelligence and colonoscopy experience: lessons from two randomised trials

Gut ◽

10.1136/gutjnl-2021-324471 ◽

2021 ◽

pp. gutjnl-2021-324471

Author(s):

Alessandro Repici ◽

Marco Spadaccini ◽

Giulio Antonelli ◽

Loredana Correale ◽

Roberta Maselli ◽

...

Keyword(s):

Artificial Intelligence ◽

Controlled Trial ◽

Randomised Trial ◽

Analysis Data ◽

Control Group ◽

Minor Role ◽

High Definition ◽

Equal Distribution ◽

Adenoma Detection ◽

Randomised Controlled

Background and aimsArtificial intelligence has been shown to increase adenoma detection rate (ADR) as the main surrogate outcome parameter of colonoscopy quality. To which extent this effect may be related to physician experience is not known. We performed a randomised trial with colonoscopists in their qualification period (AID-2) and compared these data with a previously published randomised trial in expert endoscopists (AID-1).MethodsIn this prospective, randomised controlled non-inferiority trial (AID-2), 10 non-expert endoscopists (<2000 colonoscopies) performed screening/surveillance/diagnostic colonoscopies in consecutive 40–80 year-old subjects using high-definition colonoscopy with or without a real-time deep-learning computer-aided detection (CADe) (GI Genius, Medtronic). The primary outcome was ADR in both groups with histology of resected lesions as reference. In a post-hoc analysis, data from this randomised controlled trial (RCT) were compared with data from the previous AID-1 RCT involving six experienced endoscopists in an otherwise similar setting.ResultsIn 660 patients (62.3±10 years; men/women: 330/330) with equal distribution of study parameters, overall ADR was higher in the CADe than in the control group (53.3% vs 44.5%; relative risk (RR): 1.22; 95% CI: 1.04 to 1.40; p<0.01 for non-inferiority and p=0.02 for superiority). Similar increases were seen in adenoma numbers per colonoscopy and in small and distal lesions. No differences were observed with regards to detection of non-neoplastic lesions. When pooling these data with those from the AID-1 study, use of CADe (RR 1.29; 95% CI: 1.16 to 1.42) and colonoscopy indication, but not the level of examiner experience (RR 1.02; 95% CI: 0.89 to 1.16) were associated with ADR differences in a multivariate analysis.ConclusionsIn less experienced examiners, CADe assistance during colonoscopy increased ADR and a number of related polyp parameters as compared with the control group. Experience appears to play a minor role as determining factor for ADR.Trial registration numberNCT:04260321.

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Artificial Intelligence & Machine Learning in Computer Vision Applications

Embedded Selforganising Systems ◽

10.14464/ess71432 ◽

2020 ◽

Vol 7 (1) ◽

pp. 2-3

Author(s):

Shadi Saleh

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Large Scale ◽

Short Term Memory ◽

Graphics Processing Unit ◽

Multimedia Data ◽

Processing Unit

Deep learning and machine learning innovations are at the core of the ongoing revolution in Artificial Intelligence for the interpretation and analysis of multimedia data. The convergence of large-scale datasets and more affordable Graphics Processing Unit (GPU) hardware has enabled the development of neural networks for data analysis problems that were previously handled by traditional handcrafted features. Several deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM)/Gated Recurrent Unit (GRU), Deep Believe Networks (DBN), and Deep Stacking Networks (DSNs) have been used with new open source software and libraries options to shape an entirely new scenario in computer vision processing.

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Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis

Endoscopy ◽

10.1055/a-1201-7165 ◽

2020 ◽

Author(s):

Ishita Barua ◽

Daniela Guerrero Vinsard ◽

Henriette C. Jodal ◽

Magnus Løberg ◽

Mette Kalager ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Cancer ◽

Systematic Review ◽

Meta Analysis ◽

Mean Difference ◽

Polyp Detection ◽

Detection Rates ◽

Detection Systems ◽

Adenoma Detection ◽

Advanced Adenomas

Abstract Background Artificial intelligence (AI)-based polyp detection systems are used during colonoscopy with the aim of increasing lesion detection and improving colonoscopy quality. Patients and methods: We performed a systematic review and meta-analysis of prospective trials to determine the value of AI-based polyp detection systems for detection of polyps and colorectal cancer. We performed systematic searches in MEDLINE, EMBASE, and Cochrane CENTRAL. Independent reviewers screened studies and assessed eligibility, certainty of evidence, and risk of bias. We compared colonoscopy with and without AI by calculating relative and absolute risks and mean differences for detection of polyps, adenomas, and colorectal cancer. Results Five randomized trials were eligible for analysis. Colonoscopy with AI increased adenoma detection rates (ADRs) and polyp detection rates (PDRs) compared to colonoscopy without AI (values given with 95 %CI). ADR with AI was 29.6 % (22.2 % – 37.0 %) versus 19.3 % (12.7 % – 25.9 %) without AI; relative risk (RR] 1.52 (1.31 – 1.77), with high certainty. PDR was 45.4 % (41.1 % – 49.8 %) with AI versus 30.6 % (26.5 % – 34.6 %) without AI; RR 1.48 (1.37 – 1.60), with high certainty. There was no difference in detection of advanced adenomas (mean advanced adenomas per colonoscopy 0.03 for each group, high certainty). Mean adenomas detected per colonoscopy was higher for small adenomas (≤ 5 mm) for AI versus non-AI (mean difference 0.15 [0.12 – 0.18]), but not for larger adenomas (> 5 – ≤ 10 mm, mean difference 0.03 [0.01 – 0.05]; > 10 mm, mean difference 0.01 [0.00 – 0.02]; high certainty). Data on cancer are unavailable. Conclusions AI-based polyp detection systems during colonoscopy increase detection of small nonadvanced adenomas and polyps, but not of advanced adenomas.

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The analysis of multilevel factors affecting adenoma detection rates for colonoscopies: a large-scale retrospective study

BMC Gastroenterology ◽

10.1186/s12876-021-01983-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Liang Huang ◽

Yue Hu ◽

Shan Liu ◽

Bo Jin ◽

Bin Lu

Keyword(s):

Retrospective Study ◽

Large Scale ◽

Multivariate Regression Analysis ◽

Strongly Correlated ◽

Detection Rates ◽

Factors Affecting ◽

Adenoma Detection ◽

Adenoma Detection Rates ◽

Colonoscopy Quality ◽

The Impact

Abstract Background Adenoma detection rate (ADR) is a validated primary quality indicator for colonoscopy procedures. However, there is growing concern over the variability associated with ADR indicators. Currently, the factors that influence ADRs are not well understood. Aims In this large-scale retrospective study, the impact of multilevel factors on the quality of ADR-based colonoscopy was assessed. Methods A total of 10,788 patients, who underwent colonoscopies performed by 21 endoscopists between January 2019 and December 2019, were retrospectively enrolled in this study. Multilevel factors, including patient-, procedure-, and endoscopist-level characteristics were analyzed to determine their relationship with ADR. Results The overall ADR was 20.21% and ranged from 11.4 to 32.8%. Multivariate regression analysis revealed that higher ADRs were strongly correlated with the following multilevel factors: patient age per stage (OR 1.645; 95% CI 1.577–1.717), male gender (OR 1.959; 95% CI 1.772–2.166), sedation (OR 1.402; 95% CI 1.246–1.578), single examiner colonoscopy (OR 1.330; 95% CI 1.194–1.482) and senior level endoscopists (OR 1.609; 95% CI 1.449–1.787). Conclusion The ADR is positively correlated with senior level endoscopists and single examiner colonoscopies in patients under sedation. As such, these procedure- and endoscopist-level characteristics are important considerations to improve the colonoscopy quality.

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