Deep learning in gastric tissue diseases: a systematic review

Gonçalves, Wanderson Gonçalves e; Santos, Marcelo Henrique de Paula dos; Lobato, Fábio Manoel França; Ribeiro-dos-Santos, Ândrea; Araújo, Gilderlanio Santana de

doi:10.1136/bmjgast-2019-000371

Cited by 28 publications

(25 citation statements)

References 70 publications

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“…Global accuracy may be predisposed to bias in training or test data. Therefore, other evaluation metrics are combined for a solid decision and model adoption [33,34].…”

Section: Discussionmentioning

confidence: 99%

AmazonForest: In-silico Meta-Prediction of Pathogenic Variants

Palheta¹,

Wg²,

Lm³

et al. 2020

Preprint

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ClinVar is a web platform that stores around 774k curated entries, which allows exploring genetic variants and their associations with complex phenotypes. A partial set of ClinVar’s genetic associations were reported with conflict of interpretation or uncertain clinical impact significance, which currently challenges clinicians and geneticists. Here, we evaluate the performance of data pre-processing methods combined with classical prediction methods, such as Naive Bayes, Random Forest, and Support Vector Machine to build a meta-prediction model aiming to improve genetic pathogenicity interpretation. Models were trained with ClinVar data (September 2020), and genetic variants were annotated with eight functional impact predictors catalogued with SnpEff/SnpSift (v4.3). A 10-fold cross-validation strategy was performed for evaluation by accuracy, F1-Score, Receiver Operating Characteristic, Area Under Curve. The best meta-prediction model raises by combining one-hot encoding with tree-based classifiers as Random Forest, which shows Area Under Curve ≥ 0,93. We predict pathogenicity for 109k genetic variants, which were found labeled as uncertain significance or conflict of interpretation. Additionally, we implemented AmazonForest (https://www.lghm.ufpa.br/amazonforest), a web tool to query data for a set of 5k variants that were predicted with high pathogenic probability (RFprob >= 0.9).

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“…Global accuracy may be predisposed to bias in training or test data. Therefore, other evaluation metrics are combined for a solid decision and model adoption [33,34].…”

Section: Discussionmentioning

confidence: 99%

AmazonForest: In-silico Meta-Prediction of Pathogenic Variants

Palheta¹,

Wg²,

Lm³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Many reviews included data collected from electronic medical records, hospital information systems, or any databank that used individual patient data to create predictive models or evaluate collective patterns [12,13,[16][17][18][19][20][21][24][25][26][27]30,[33][34][35]37,38,40,[42][43][44][45]. Additionally, four reviews included primary studies based on imaging datasets and databanks, assessing different parameters of accuracy [15,29,31,36]. Other reviews focused on genetic databases [28,35], data from assisted reproductive technologies [30], or publicly available data [11,14,22,32].…”

Section: Data Sources and Purposes Of Included Studiesmentioning

confidence: 99%

“…Most of the studies assessed the effects of big data analytics on noncommunicable diseases [12][13][14][15]17,21,22,24,27,31,32,34,36,38,[40][41][42][43][44]. Furthermore, three reviews covered mental health, associated with the indicator "suicide mortality rate" [19,25,45]; three studies were related to the indicator "probability of dying from any of cardiovascular, cancer, diabetes, or chronic renal disease" [16,18,20,28,29]; and two studies were related to the indicator "proportion of bloodstream infections due to antimicrobial-resistant organisms" [26,33].…”

Section: Who Indicators and Core Prioritiesmentioning

confidence: 99%

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Nascimento¹,

Marcolino²,

Abdulazeem³

et al. 2021

J Med Internet Res

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Background Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health. Objective The aim of this study was to assess the impact of the use of big data analytics on people’s health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2–related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people’s health. Methods Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist. Results The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. “Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease” and “suicide mortality rate” were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as “critically low” for 25 reviews, as “low” for 7 reviews, and as “moderate” for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data. Conclusions Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048

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“…Although clinical evidence of deep learning algorithms is still poor (there are few studies with low confidence), they show equivalent (or even better) performance than clinicians [9,10]. In this sense, some systematic reviews show the potential of deep learning in gastric tissue diseases [11] and wireless endoscopic capsule [12], but these studies also identify risk of bias due to gaps in the evaluation metrics and public availability of the dataset [11] that must be solved through prospective multicenter studies [12].…”

Section: Introductionmentioning

confidence: 99%

Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation

et al. 2020

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Colorectal cancer is one of the leading cancer death causes worldwide, but its early diagnosis highly improves the survival rates. The success of deep learning has also benefited this clinical field. When training a deep learning model, it is optimized based on the selected loss function. In this work, we consider two networks (U-Net and LinkNet) and two backbones (VGG-16 and Densnet121). We analyzed the influence of seven loss functions and used a principal component analysis (PCA) to determine whether the PCA-based decomposition allows for the defining of the coefficients of a non-redundant primal loss function that can outperform the individual loss functions and different linear combinations. The eigenloss is defined as a linear combination of the individual losses using the elements of the eigenvector as coefficients. Empirical results show that the proposed eigenloss improves the general performance of individual loss functions and outperforms other linear combinations when Linknet is used, showing potential for its application in polyp segmentation problems.

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Deep learning in gastric tissue diseases: a systematic review

Cited by 28 publications

References 70 publications

AmazonForest: In-silico Meta-Prediction of Pathogenic Variants

AmazonForest: In-silico Meta-Prediction of Pathogenic Variants

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation

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