Computational learning of features for automated colonic polyp classification

A non-aligned translation with a neoplastic classifier regularization to include vascular NBI patterns in standard colonoscopies

Polyp vascular patterns are key to categorizing colorectal cancer malignancy. These patterns are typically observed in situ from specialized narrow-band images (NBI). Nonetheless, such vascular characterization is lost from standard colonoscopies (the primary attention mechanism). Besides, even for NBI observations, the categorization remains biased for expert observations, reporting errors in classification from 59.5% to 84.2%. This work introduces an end-to-end computational strategy to enhance in situ standard colonoscopy observations, including vascular patterns typically observed from NBI mechanisms. These retrieved synthetic images are achieved by adjusting a deep representation under a non-aligned translation task from optical colonoscopy (OC) to NBI. The introduced scheme includes an architecture to discriminate enhanced neoplastic patterns achieving a remarkable separation into the embedding representation. The proposed approach was validated in a public dataset with a total of 76 sequences, including standard optical sequences and the respective NBI observations. The enhanced optical sequences were automatically classified among adenomas and hyperplastic samples achieving an F1-score of 0.86%. To measure the sensibility capability of the proposed approach, serrated samples were projected to the trained architecture. In this experiment, statistical differences from three classes with a ρ-value <0.05 were reported, following a Mann-Whitney U test. This work showed remarkable polyp discrimination results in enhancing OC sequences regarding typical NBI patterns. This method also learns polyp class distributions under the unpaired criteria (close to real practice), with the capability to separate serrated samples from adenomas and hyperplastic ones.

An exploratory simulation study and prediction model on human brain behavior and activity using an integration of deep neural network and biosensor Rabi antenna

The plasmonic antenna probe is constructed using a silver rod embedded in a modified Mach-Zehnder interferometer (MZI) ad-drop filter. Rabi antennas are formed when space-time control reaches two levels of system oscillation and can be used as human brain sensor probes. Photonic neural networks are designed using brain-Rabi antenna communication, and transmissions are connected via neurons. Communication signals are carried by electron spin (up and down) and adjustable Rabi frequency. Hidden variables and deep brain signals can be obtained by external detection. A Rabi antenna has been developed by simulation using computer simulation technology (CST) software. Additionally, a communication device has been developed that uses the Optiwave program with Finite-Difference Time-Domain (OptiFDTD). The output signal is plotted using the MATLAB program with the parameters of the OptiFDTD simulation results. The proposed antenna oscillates in the frequency range of 192 THz to 202 THz with a maximum gain of 22.4 dBi. The sensitivity of the sensor is calculated along with the result of electron spin and applied to form a human brain connection. Moreover, intelligent machine learning algorithms are proposed to identify high-quality transmissions and predict the behavior of transmissions in the near future. During the process, a root mean square error (RMSE) of 2.3332(±0.2338) was obtained. Finally, it can be said that our proposed model can efficiently predict human mind, thoughts, behavior as well as action/reaction, which can be greatly helpful in the diagnosis of various neuro-degenerative/psychological diseases (such as Alzheimer's, dementia, etc.) and for security purposes.

A Reliable and Sensitive Framework for Simultaneous Type and Stage Detection of Colorectal Cancer Polyps

With the goal of enhancing the early diagnosis of colorectal cancer (CRC) polyps and reducing the risk of mortality in cancer patients, in this article, we present a unique diagnosis framework including a Vision-based Surface Tactile Sensor (VS-TS) and complementary Artificial Intelligence algorithms. Leveraging the morphological characteristics (i.e., shape and texture) and stiffness features of the CRC polyps, the proposed framework is able to reliably and sensitively identify their type and stage. To thoroughly characterize and identify the required VS-TS sensitivity for reliable identification of polyps, we first fabricated three different VS-TSs and qualitatively evaluated their performances on 48 different types of polyp phantoms fabricated based on four different types of realistic CRC polyps and three different materials. Next, to quantitatively compare the performance and sensitivity of the fabricated VS-TSs, we used Support Vector Machine (SVM) algorithm and employed various statistical metrics (i.e., accuracy, reliability, and sensitivity). Next, using the most sensitive VS-TS, we classified the type of tumors using the SVM algorithm and applied the t-Distributed Stochastic Neighbor Embedding algorithm to successfully identify the stiffness of classified polyp phantoms solely based on the output images of the VS-TS sensor. Results demonstrated that an SVM algorithm applied on the image outputs of a VS-TS with a Shore hardness of 00-40 scale is able to classify all types of polyps with > 90% accuracy, sensitivity, and reliability. We also repeated experiments on samples of ex-vivo lamb tripe tissues and successfully verified the high sensitivity and reliability of the proposed framework (i.e., > 94%).

Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability

BACKGROUND AND AIMS

Publicly available databases containing colonoscopic imaging data are valuable resources for artificial intelligence (AI) research. Currently, little is known regarding the available number and content of these databases. This review aimed to describe the availability, accessibility, and usability of publicly available colonoscopic imaging databases, focusing on polyp detection, polyp characterization, and quality of colonoscopy.

METHODS

A systematic literature search was performed in MEDLINE and Embase to identify AI studies describing publicly available colonoscopic imaging databases published after 2010. Second, a targeted search using Google's Dataset Search, Google Search, GitHub, and Figshare was done to identify databases directly. Databases were included if they contained data about polyp detection, polyp characterization, or quality of colonoscopy. To assess accessibility of databases, the following categories were defined: open access, open access with barriers, and regulated access. To assess the potential usability of the included databases, essential details of each database were extracted using a checklist derived from the Checklist for Artificial Intelligence in Medical Imaging.

RESULTS

We identified 22 databases with open access, 3 databases with open access with barriers, and 15 databases with regulated access. The 22 open access databases contained 19,463 images and 952 videos. Nineteen of these databases focused on polyp detection, localization, and/or segmentation; 6 on polyp characterization, and 3 on quality of colonoscopy. Only half of these databases have been used by other researcher to develop, train, or benchmark their AI system. Although technical details were in general well reported, important details such as polyp and patient demographics and the annotation process were under-reported in almost all databases.

CONCLUSIONS

This review provides greater insight on public availability of colonoscopic imaging databases for AI research. Incomplete reporting of important details limits the ability of researchers to assess the usability of current databases.

Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques

Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.

Unsupervised Feature Selection Using an Integrated Strategy of Hierarchical Clustering With Singular Value Decomposition: An Integrative Biomarker Discovery Method With Application to Acute Myeloid Leukemia

In this article, we propose a novel unsupervised feature selection method by combining hierarchical feature clustering with singular value decomposition (SVD). The proposed algorithm first generates several feature clusters by adopting the hierarchical clustering on the feature space and then applies SVD to each of these feature clusters to find out the feature that contributes most to the SVD-entropy. The proposed feature selection method selects an optimal feature subset that not only minimizes the mutual dependency among the selected features but also maximizes the mutual dependency of the selected features against their nearest neighbor non-selected features to some extent. Each of the selected features also contributes the maximum SVD-entropy among all features of the same feature cluster. The experimental results demonstrate that the proposed algorithm performs well against several state-of-the-art methods of feature selection in terms of various evaluation criteria such as classification accuracy, redundancy rate, and representation entropy. The superiority of the proposed algorithm is demonstrated through analysis of Acute Myeloid Leukemia (AML) multi-omics data that consist of five datasets: gene expression, exon expression, methylation, microRNA, and pathway activity dataset (paradigm IPLs) from The Cancer Genome Atlas (TCGA). Our analysis pinpoints a candidate gene-marker, EREG for AML with an integrative omics evidence. EREG is targeted by two top ranked microRNAs, hsa-miR-1286 and hsa-miR-1976, here in the datasets. The method and results will be useful for biomarker discovery in the era of in precision medicine.

Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification

Cancer is the deadliest disease among all the diseases and the main cause of human mortality. Several types of cancer sicken the human body and affect organs. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. The early diagnosis of stomach cancer is essential to reduce the mortality rate. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. Many computerized techniques have been introduced in the literature but due to a few challenges (i.e., high similarity among the healthy and infected regions, irrelevant features extraction, and so on), there is much room to improve the accuracy and reduce the computational time. In this paper, a deep-learning-based stomach disease classification method employing deep feature extraction, fusion, and optimization using WCE images is proposed. The proposed method comprises several phases: data augmentation performed to increase the dataset images, deep transfer learning adopted for deep features extraction, feature fusion performed on deep extracted features, fused feature matrix optimized with a modified dragonfly optimization method, and final classification of the stomach disease was performed. The features extraction phase employed two pre-trained deep CNN models (Inception v3 and DenseNet-201) performing activation on feature derivation layers. Later, the parallel concatenation was performed on deep-derived features and optimized using the meta-heuristic method named the dragonfly algorithm. The optimized feature matrix was classified by employing machine-learning algorithms and achieved an accuracy of 99.8% on the combined stomach disease dataset. A comparison has been conducted with state-of-the-art techniques and shows improved accuracy.

A systematic review on application of deep learning in digestive system image processing

With the advent of the big data era, the application of artificial intelligence represented by deep learning in medicine has become a hot topic. In gastroenterology, deep learning has accomplished remarkable accomplishments in endoscopy, imageology, and pathology. Artificial intelligence has been applied to benign gastrointestinal tract lesions, early cancer, tumors, inflammatory bowel diseases, livers, pancreas, and other diseases. Computer-aided diagnosis significantly improve diagnostic accuracy and reduce physicians' workload and provide a shred of evidence for clinical diagnosis and treatment. In the near future, artificial intelligence will have high application value in the field of medicine. This paper mainly summarizes the latest research on artificial intelligence in diagnosing and treating digestive system diseases and discussing artificial intelligence's future in digestive system diseases. We sincerely hope that our work can become a stepping stone for gastroenterologists and computer experts in artificial intelligence research and facilitate the application and development of computer-aided image processing technology in gastroenterology.

MiR-3614-5p Is a Potential Novel Biomarker for Colorectal Cancer

MiR-3614-5p has been found in a variety of cancers including colorectal cancer. However, the association of miR-3614-5p with colorectal cancer is still unclear. Based on the Cancer Genome Atlas (TCGA) database, the relationship between miR-3614-5p and colorectal cancer can be proved. Wilcoxon rank-sum test was used to compare the miR-3614-5p expression in colorectal cancer tissues and under normal conditions, respectively. The logistic regression method was further employed to analyze the relationship between miR-3614-5p and clinicopathological characteristics. Also, the correlation between miR-3614-5p and survival rate was evaluated by Kaplan-Meier and Cox regression analysis. Besides, gene set enrichment analysis (GSEA) was used to investigate the biological functions of miR-3614-5p. The decrease of miR-3614-5p expression of colorectal cancer was significantly correlated with N stage (OR) = 0.7 for N1&N2 vs. N0), M stage (OR = 0.5 for M1 vs. M0), pathologic stage (OR = 0.7 for Stage III & Stage IV vs. Stage I & Stage II), neoplasm type (OR = 0.5 for rectum adenocarcinoma vs. colon adenocarcinoma), and lymphatic invasion (OR = 0.6 for YES vs. NO) (all p-values < 0.05). Kaplan-Meier survival analysis showed that colorectal cancer with low miR-3614-5p has a poorer prognosis than that of high miR-3614-5p (p = 0.005). According to univariate analysis, low miR-3614-5p was associated with poor overall survival (OS) [hazard ratio (HR) = 0.599; 95% confidence interval (CI): 0.418-0.857; p = 0.005]. In multivariate analysis, miR-3614-5p was closely related to OS (HR = 0.630; 95% CI: 0.405-0.978, p = 0.021). GSEA showed that the high expression phenotype of miR-3614-5p differentially enriches the P53 pathway. Meanwhile, the high expression phenotype of miR-3614-5p enhanced NK T cell activation, negative T cell selection, response to interleukin 2, and response to tumor cells. MiR-3614-5p is a possible prognostic marker of low survival rate for patients with colorectal cancer. Moreover, the P53 pathway and P38MAPK pathway may be the key pathways regulated by miR-3614-5p in colorectal cancer.