Challenges Facing the Detection of Colonic Polyps: What Can Deep Learning Do?

PFPRNet: A Phase-Wise Feature Pyramid With Retention Network for Polyp Segmentation

Early detection of colonic polyps is crucial for the prevention and diagnosis of colorectal cancer. Currently, deep learning-based polyp segmentation methods have become mainstream and achieved remarkable results. Acquiring a large number of labeled data is time-consuming and labor-intensive, and meanwhile the presence of numerous similar wrinkles in polyp images also hampers model prediction performance. In this paper, we propose a novel approach called Phase-wise Feature Pyramid with Retention Network (PFPRNet), which leverages a pre-trained Transformer-based Encoder to obtain multi-scale feature maps. A Phase-wise Feature Pyramid with Retention Decoder is designed to gradually integrate global features into local features and guide the model's attention towards key regions. Additionally, our custom Enhance Perception module enables capturing image information from a broader perspective. Finally, we introduce an innovative Low-layer Retention module as an alternative to Transformer for more efficient global attention modeling. Evaluation results on several widely-used polyp segmentation datasets demonstrate that our proposed method has strong learning ability and generalization capability, and outperforms the state-of-the-art approaches.

Exploring the Mechanism of Canmei Formula in Preventing and Treating Recurrence of Colorectal Adenoma Based on Data Mining and Algorithm Prediction

BACKGROUND

The high incidence of recurrence and malignant transformation of colorectal adenoma (CRA) are current issues that need to be addressed in clinical practice. Canmei Formula (CMF) has shown promising results in the prevention and treatment, however, it lacks effective clinical data support and its mechanism of action is not fully elucidated.

OBJECTIVE

The aim of this study is to evaluate the clinical efficacy and safety of CMF in preventing and treating CRA, and to explore its effective chemical components and pharmacological mechanisms.

METHOD

A randomized controlled clinical trial was conducted, with patients diagnosed with CRA within 6 months as the study subjects. After randomization, the patients were divided into a treatment group (receiving CMF granules) or a control group (receiving berberine hydrochloride tablets). The one-year recurrence rate of CRA was used as the key efficacy indicator to assess the effectiveness of CMF in preventing and treating CRA. The chemical components of CMF were identified using the UFLC-Q-TOF-MS/MS combined system. Data mining and the wSDTNBI algorithm were combined to construct a differential expression gene (DEG) - CMF prediction target interaction network for CRA. The core targets of CMF in CRA prevention and treatment were identified through topological analysis, and validated using molecular docking and in vitro experiments.

RESULT

During the period from October 1 2021 to December 31 2023, a total of 228 participants were included in the study. After block randomization, 114 patients were assigned to each group. In the treatment group, 98 patients completed follow-up examinations, with 16 patients (14.0%) exhibiting shedding, Adenoma recurrence was identified in 24 (24.5%) patients through colonoscopy. In the control group, 99 cases completed the follow-up examination, while 15 cases (13.2%) were lost to follow-up. There were 45 cases (45.5%) experienced recurrence of adenomas. During the follow-up period, no cases of colorectal cancer or severe adverse reactions were reported. UFLC-QTOF-MS/MS identification was combined with traditional Chinese medicine database mining to obtain 192 active chemical components of Canmei Formula. Using the wSDTNBI algorithm, 1044 prediction targets were predicted, and 3308 differentially expressed genes of CRA were extracted from the TCGA database. Network topology analysis and bioinformatics analysis were performed on 164 intersecting core targets. Molecular docking and qPCR analysis revealed that CMF downregulates angiotensin II type 1 receptor (AT1R) and regulated interleukin-8 (CXCL8) and matrix metalloproteinase 13 (MMP13) within the REN/Ang II/AT1R axis of the renin-angiotensin signaling pathway, thereby preventing and treating CRA.

CONCLUSION

This small-scale randomized controlled clinical trial showed that CMF granules can safely and effectively reduce the risk of CRA recurrence. CMF prevents and treats colorectal adenomas by modulating the renin-angiotensin signaling pathway and the inflammatory response.

Water-assisted colonoscopy in inflammatory bowel diseases: From technical implications to diagnostic and therapeutic potentials

Water-assisted colonoscopy (WAC) application in inflammatory bowel diseases (IBD) endoscopy offers significant technical opportunities. Traditional gas-aided insufflation colonoscopy increases patient discomfort, presenting challenges in the frequent and detailed mucosal assessments required for IBD endoscopy. WAC techniques, including water immersion and exchange, provide superior patient comfort and enhanced endoscopic visualisation. WAC effectively reduces procedural pain, enhances bowel cleanliness, and increases adenoma detection rates, which is crucial for colorectal cancer screening and disease-related evaluations in IBD patients. Additionally, underwater techniques facilitate basic and advanced endoscopic resections, such as polypectomy and endoscopic mucosal and submucosal resections, often required for resecting IBD-associated neoplasia.

Applying Deep-Learning Algorithm Interpreting Kidney, Ureter, and Bladder (KUB) X-Rays to Detect Colon Cancer

Early screening is crucial in reducing the mortality of colorectal cancer (CRC). Current screening methods, including fecal occult blood tests (FOBT) and colonoscopy, are primarily limited by low patient compliance and the invasive nature of the procedures. Several advanced imaging techniques such as computed tomography (CT) and histological imaging have been integrated with artificial intelligence (AI) to enhance the detection of CRC. There are still limitations because of the challenges associated with image acquisition and the cost. Kidney, ureter, and bladder (KUB) radiograph which is inexpensive and widely used for abdominal assessments in emergency settings and shows potential for detecting CRC when enhanced using advanced techniques. This study aimed to develop a deep learning model (DLM) to detect CRC using KUB radiographs. This retrospective study was conducted using data from the Tri-Service General Hospital (TSGH) between January 2011 and December 2020, including patients with at least one KUB radiograph. Patients were divided into development (n = 28,055), tuning (n = 11,234), and internal validation (n = 16,875) sets. An additional 15,876 patients were collected from a community hospital as the external validation set. A 121-layer DenseNet convolutional network was trained to classify KUB images for CRC detection. The model performance was evaluated using receiver operating characteristic curves, with sensitivity, specificity, and area under the curve (AUC) as metrics. The AUC, sensitivity, and specificity of the DLM in the internal and external validation sets achieved 0.738, 61.3%, and 74.4%, as well as 0.656, 47.7%, and 72.9%, respectively. The model performed better for high-grade CRC, with AUCs of 0.744 and 0.674 in the internal and external sets, respectively. Stratified analysis showed superior performance in females aged 55-64 with high-grade cancers. AI-positive predictions were associated with a higher long-term risk of all-cause mortality in both validation cohorts. AI-enhanced KUB X-ray analysis can enhance CRC screening coverage and effectiveness, providing a cost-effective alternative to traditional methods. Further prospective studies are necessary to validate these findings and fully integrate this technology into clinical practice.

A Novel Soft and Inflatable Strain-based Tactile Sensing Balloon for Enhanced Diagnosis of Colorectal Cancer Polyps Via Colonoscopy

In this paper, with the goal of addressing the lack of tactile feedback in colorectal cancer (CRC) polyps diagnosis using a colonoscopy procedure, we propose the design and fabrication of a novel soft and inflatable strain-based tactile sensing balloon (SI-STSB). The proposed soft sensor features a unique stretchable sensing layer - that utilizes a liquid metal injected within spiral-shape microchannels of a stretchable substrate - and is integrated with a unique inflatable balloon mechanism. The proposed SI-STSB has been thoroughly characterized through different calibration experiments. Results demonstrate a phenomenal adjustable sensitivity with low hysteresis behavior under different experimental conditions for this sensor making it a great candidate for enhancing the existing diagnosis procedures.

Design and Development of a Novel Soft and Inflatable Tactile Sensing Balloon for Early Diagnosis of Colorectal Cancer Polyps

In this paper, with the goal of addressing the high early-detection miss rate of colorectal cancer (CRC) polyps during a colonoscopy procedure, we propose the design and fabrication of a unique inflatable vision-based tactile sensing balloon (VTSB). The proposed soft VTSB can readily be integrated with the existing colonoscopes and provide a radiation-free, safe, and high-resolution textural mapping and morphology characterization of CRC polyps. The performance of the proposed VTSB has been thoroughly characterized and evaluated on four different types of additively manufactured CRC polyp phantoms with three different stiffness levels. Additionally, we integrated the VTSB with a colonoscope and successfully performed a simulated colonoscopic procedure inside a tube with a few CRC polyp phantoms attached to its internal surface.

The role of deep learning in diagnosing colorectal cancer

Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.

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%).

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022)

Background

Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.

Objective

This study uses bibliometrics to analyze research in AI in CRC, summarize the field's history and current status of research, and predict future research directions.

Method

We searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results.

Result

This study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field's most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination.

Conclusion

AI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.

Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore

BACKGROUND

Colonoscopy is a mainstay to detect premalignant neoplastic lesions in the colon. Real-time Artificial Intelligence (AI)-aided colonoscopy purportedly improves the polyp detection rate, especially for small flat lesions. The aim of this study is to evaluate the performance of real-time AI-aided colonoscopy in the detection of colonic polyps.

METHODS

A prospective single institution cohort study was conducted in Singapore. All real-time AI-aided colonoscopies, regardless of indication, performed by specialist-grade endoscopists were anonymously recorded from July to September 2021 and reviewed by 2 independent authors (FHK, JL). Sustained detection of an area by the program was regarded as a "hit". Histology for the polypectomies were reviewed to determine adenoma detection rate (ADR). Individual endoscopist's performance with AI were compared against their baseline performance without AI endoscopy.

RESULTS

A total of 24 (82.8%) endoscopists participated with 18 (62.1%) performing ≥ 5 AI-aided colonoscopies. Of the 18, 72.2% (n = 13) were general surgeons. During that 3-months period, 487 "hits" encountered in 298 colonoscopies. Polypectomies were performed for 51.3% and 68.4% of these polypectomies were adenomas on histology. The post-intervention median ADR was 30.4% was higher than the median baseline polypectomy rate of 24.3% (p = 0.02). Of the adenomas excised, 14 (5.6%) were sessile serrated adenomas. Of those who performed ≥ 5 AI-aided colonoscopies, 13 (72.2%) had an improvement of ADR compared to their polypectomy rate before the introduction of AI, of which 2 of them had significant improvement.

CONCLUSIONS

Real-time AI-aided colonoscopy have the potential to improved ADR even for experienced endoscopists and would therefore, improve the quality of colonoscopy.

A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques

An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics' potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.

Polyp detection in video colonoscopy using deep learning

Video colonoscopy automatic processing is a challenge and further development of computer assisted diagnosis is very helpful in correctness assessment of the exam, in e-learning and training, for statistics on polyps’ malignity or in polyps’ survey. New devices and programming languages are emerging and deep learning begun already to furnish astonishing results, in the quest for high speed and optimal polyp detection software. This paper presents a successful attempt in detecting the intestinal polyps in real time video colonoscopy with deep learning, using Mobile Net.

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

Medical Applications of Artificial Intelligence (Legal Aspects and Future Prospects)

Background: Cutting-edge digital technologies are being actively introduced into healthcare. The recent successful efforts of artificial intelligence in diagnosing, predicting and studying diseases, as well as in surgical assisting demonstrate its high efficiency. The AI’s ability to promptly take decisions and learn independently has motivated large corporations to focus on its development and gradual introduction into everyday life. Legal aspects of medical activities are of particular importance, yet the legal regulation of AI’s performance in healthcare is still in its infancy. The state is to a considerable extent responsible for the formation of a legal regime that would meet the needs of modern society (digital society). Objective: This study aims to determine the possible modes of AI’s functioning, to identify the participants in medical-legal relations, to define the legal personality of AI and circumscribe the scope of its competencies. Of importance is the issue of determining the grounds for imposing legal liability on persons responsible for the performance of an AI system. Results: The present study identifies the prospects for a legal assessment of AI applications in medicine. The article reviews the sources of legal regulation of AI, including the unique sources of law sanctioned by the state. Particular focus is placed on medical-legal customs and medical practices. Conclusions: The presented analysis has allowed formulating the approaches to the legal regulation of AI in healthcare.

Application of deep learning based artificial intelligence technology in identification of colorectal polyps

Colorectal cancer is a cancer type that is most suitable for screening since subjects at risk of this malignancy can clearly benefit from colonoscopy screening. In 2017, there were about 431951 new cases of colorectal cancer in China, with an increase of 203.5% in 28 years. Early detection and early removal of adenomatous polyps and other precancerous lesions during colonoscopy can prevent the occurrence of colorectal cancer. However, various factors lead to missed diagnosis of polyps during colonoscopy, which increases the risk of colorectal cancer. In recent years, with the rapid development of artificial intelligence technology in the medical field, colonoscopy assisted by artificial intelligence can increase the detection rate of polyps and improve the quality of colonoscopy. This paper mainly reviews the quality control, bowel preparation, diagnosis and classification of colorectal polyps, and the future opportunities and challenges faced by convolutional neural network based artificial intelligence technology in the field of colonoscopy, hoping to provide some reference for clinical work.

Real-time polyp detection model using convolutional neural networks

Colorectal cancer is a major health problem, where advances towards computer-aided diagnosis (CAD) systems to assist the endoscopist can be a promising path to improvement. Here, a deep learning model for real-time polyp detection based on a pre-trained YOLOv3 (You Only Look Once) architecture and complemented with a post-processing step based on an object-tracking algorithm to reduce false positives is reported. The base YOLOv3 network was fine-tuned using a dataset composed of 28,576 images labelled with locations of 941 polyps that will be made public soon. In a frame-based evaluation using isolated images containing polyps, a general F1 score of 0.88 was achieved (recall = 0.87, precision = 0.89), with lower predictive performance in flat polyps, but higher for sessile, and pedunculated morphologies, as well as with the usage of narrow band imaging, whereas polyp size < 5 mm does not seem to have significant impact. In a polyp-based evaluation using polyp and normal mucosa videos, with a positive criterion defined as the presence of at least one 50-frames-length (window size) segment with a ratio of 75% of frames with predicted bounding boxes (frames positivity), 72.61% of sensitivity (95% CI 68.99–75.95) and 83.04% of specificity (95% CI 76.70–87.92) were achieved (Youden = 0.55, diagnostic odds ratio (DOR) = 12.98). When the positive criterion is less stringent (window size = 25, frames positivity = 50%), sensitivity reaches around 90% (sensitivity = 89.91%, 95% CI 87.20–91.94; specificity = 54.97%, 95% CI 47.49–62.24; Youden = 0.45; DOR = 10.76). The object-tracking algorithm has demonstrated a significant improvement in specificity whereas maintaining sensitivity, as well as a marginal impact on computational performance. These results suggest that the model could be effectively integrated into a CAD system.

Simplifying Measurement of Adenoma Detection Rates for Colonoscopy

BACKGROUND

Adenoma detection rate (ADR) is the colonoscopy quality metric with the strongest association to interval or "missed" cancer. Accurate measurement of ADR can be laborious and costly.

AIMS

Our aim was to determine if administrative procedure codes for colonoscopy and text searches of pathology results for adenoma mentions could estimate ADR.

METHODS

We identified US Veterans with a colonoscopy using Current Procedure Terminology (CPT) codes between January 2013 and December 2016 at ten Veterans Affairs sites. We applied simple text searches using Microsoft SQL Server full-text searches to query all pathology notes for "adenoma(s)" or "adenomatous" text mentions to calculate ADRs. To validate our identification of colonoscopy procedures, endoscopists of record, and adenoma detection from the electronic health record, we manually reviewed a random sample of 2000 procedure and pathology notes from the 10 sites.

RESULTS

Structured data fields were accurate in identification of colonoscopies being performed (PPV = 0.99; 95% CI 0.99-1.00) and identifying the endoscopist of record (PPV of 0.95; 95% CI 0.94-0.96) for ADR measurement. Simple text searches of pathology notes for adenoma mentions had excellent performance statistics as follows: sensitivity 0.99 (95% CI 0.98-1.00), specificity 0.93 (95% CI 0.92-0.95), NPV 0.99 (95% CI 0.98-1.00), and PPV 0.93 (0.91-0.94) for measurement of ADR. There was no clinically significant difference in the estimates of overall ADR vs. screening ADR (p > 0.05).

CONCLUSIONS

Measuring ADR using administrative codes and text searches from pathology results is an efficient method to broadly survey colonoscopy quality.

Patient, Physician, and Procedure Characteristics Are Independently Predictive of Polyp Detection Rates in Clinical Practice

BACKGROUND

Variability in colon polyp detection impacts patient outcomes. However, the relative influence of physician, patient, and procedure-specific factors on polyp detection is unclear. Therefore, determining how these factors contribute to adenoma and sessile serrated polyp (SSP) detection is important to contextualize measures of colonoscopy quality such as adenoma detection rate and patient outcomes.

AIMS

To determine the relative contribution of physician, patient, and procedure-specific factors in total polyp, adenoma, and SSP detection rates.

METHODS

We performed a retrospective study of patients undergoing screening colonoscopy and used a two-level generalized linear mixed regression model to identify factors associated with polyp detection.

RESULTS

7799 average risk screening colonoscopies were performed between July 2016 and October 2017. The patient factor most strongly associated with increased risk of adenoma and sessile serrated polyp detection was white race (OR 1.21, 95% CI 1.05-1.39 and OR 3.17, 95% CI 2.34-4.30, respectively). Adenomatous (OR 1.92, 95% CI 1.04-3.57) and sessile serrated polyps (OR 5.56, 95% CI 1.37-20.0) were more likely to be found during procedures performed with anesthesia care as compared to those with moderate sedation. Physician with a luminal gastrointestinal focus had increased odds of adenoma detection (OR 1.61, 95% CI 1.02-2.50).

CONCLUSIONS

In a multi-level model accounting for clustering effects, we identified patient, provider and procedural factors independently influence adenoma and sessile serrated polyp detection. Our findings suggest that to compare polyp detection rates between endoscopists, even at the same institution, risk adjustment by characteristics of the patient population and practice is necessary.

Comparable quality of bowel preparation with single-day versus three-day low-residue diet: Randomized controlled trial

BACKGROUND AND AIMS

There is controversy about the length of low-residue diet (LRD) for colonoscopy preparation. The aim of the study was to compare one-day vs. three-day LRD associated to standard laxative treatment for achieving an adequate colonoscopy preparation in average risk subjects with positive fecal immunochemical test undergoing screening colonoscopy.

METHODS

A non-inferiority, randomized, controlled, parallel-group clinical trial was performed in the setting of average risk colorectal cancer screening program. Participants were randomized to receive 1-day vs. 3-day LRD in addition to standard polyethilenglicol treatment. Adequacy of preparation was evaluated using the Boston Bowel Preparation Scale (BBPS). Primary outcome was achieving a BBPS ≥ 2 in all colon segments. Analysis was performed for a non-inferiority margin of 5%, a 95% statistical power and one-sided 0.05 significance level.

RESULTS

A total of 855 patients were randomized. Adequate bowel preparation was similar between groups: 97.9% of patients in the 1-day LRD group vs 96.9% in the 3-day LRD group achieved the primary outcome (P-value for non-inferiority < 0.001). The percentage of patients with BBPS scores ≥ 8 was superior in the 1-day LRD group (254 vs 221 in the 3-day LRD group, P = 0.032). The 1-day regimen was better tolerated than the 3-day diet. 47.7% (vs 28.7%, P < 0.05) of patients rated the 1-day LRD as very easy to follow.

CONCLUSION

The 1-day LRD is non-inferior to 3-day LRD for achieving an adequate colon cleansing before average risk screening colonoscopy and it is better tolerated.

Automatic colonic polyp detection using integration of modified deep residual convolutional neural network and ensemble learning approaches

BACKGROUND AND OBJECTIVE

The increased incidence of colorectal cancer (CRC) and its mortality rate have attracted interest in the use of artificial intelligence (AI) based computer-aided diagnosis (CAD) tools to detect polyps at an early stage. Although these CAD tools have thus far achieved a good accuracy level to detect polyps, they still have room to improve further (e.g. sensitivity). Therefore, a new CAD tool is developed in this study to detect colonic polyps accurately.

METHODS

In this paper, we propose a novel approach to distinguish colonic polyps by integrating several techniques, including a modified deep residual network, principal component analysis and AdaBoost ensemble learning. A powerful deep residual network architecture, ResNet-50, was investigated to reduce the computational time by altering its architecture. To keep the interference to a minimum, median filter, image thresholding, contrast enhancement, and normalisation techniques were exploited on the endoscopic images to train the classification model. Three publicly available datasets, i.e., Kvasir, ETIS-LaribPolypDB, and CVC-ClinicDB, were merged to train the model, which included images with and without polyps.

RESULTS

The proposed approach trained with a combination of three datasets achieved Matthews Correlation Coefficient (MCC) of 0.9819 with accuracy, sensitivity, precision, and specificity of 99.10%, 98.82%, 99.37%, and 99.38%, respectively.

CONCLUSIONS

These results show that our method could repeatedly classify endoscopic images automatically and could be used to effectively develop computer-aided diagnostic tools for early CRC detection.

Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening

Hip joint ultrasonographic (US) imaging is the golden standard for developmental dysplasia of the hip (DDH) screening. However, the effectiveness of this technique is subject to interoperator and intraobserver variability. Thus, a multi-detection deep learning artificial intelligence (AI)-based computer-aided diagnosis (CAD) system was developed and evaluated. The deep learning model used a two-stage training process to segment the four key anatomical structures and extract their respective key points. In addition, the check angle of the ilium body balancing level was set to evaluate the system’s cognitive ability. Hence, only images with visible key anatomical points and a check angle within ±5° were used in the analysis. Of the original 921 images, 320 (34.7%) were deemed appropriate for screening by both the system and human observer. Moderate agreement (80.9%) was seen in the check angles of the appropriate group (Cohen’s κ = 0.525). Similarly, there was excellent agreement in the intraclass correlation coefficient (ICC) value between the measurers of the alpha angle (ICC = 0.764) and a good agreement in beta angle (ICC = 0.743). The developed system performed similarly to experienced medical experts; thus, it could further aid the effectiveness and speed of DDH diagnosis.

Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach

Colorectal cancer is the third most common and second most lethal tumor globally, causing 900,000 deaths annually. In this research, a computer aided diagnosis system was designed that detects colorectal cancer, using an innovative dataset composing of both numeric (blood and urine analysis) and qualitative data (living environment of the patient, tumor position, T, N, M, Dukes classification, associated pathology, technical approach, complications, incidents, ultrasonography-dimensions as well as localization). The intelligent computer aided colorectal cancer diagnosis system was designed using different machine learning techniques, such as classification and shallow and deep neural networks. The maximum accuracy obtained from solving the binary classification problem with traditional machine learning algorithms was 77.8%. However, the regression problem solved with deep neural networks yielded with significantly better performance in terms of mean squared error minimization, reaching the value of 0.0000529.

[Application of artificial intelligence for detection and classification of colon polyps]

Colorectal cancer is one of the most common cancers worldwide, and colonoscopy has proven to be a preferable modality for screening and surveillance of colorectal cancer. This review discusses the clinical application of artificial intelligence (AI) and computer-aided diagnosis for automated colonoscopic detection and diagnosis of colorectal polyps for better understanding of the application of AI-based computer-aided diagnosis systems especially in terms of machine learning, deep learning and convolutional neural network for screening and surveillance of colorectal cancer.

Deep Learning in Selected Cancers' Image Analysis-A Survey

Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.

"Obscure" Appendiceal Orifice Polyps Can Be Challenging to Identify by Colonoscopy

The primary purpose of screening colonoscopy is the detection and subsequent removal of precancerous polyps. However, effective recognition of appendiceal lesions with a standard endoscope is often challenging and is limited to the base of the cecum and appendiceal orifice. The majority of appendiceal polyps are found incidentally following an appendectomy, though rarely they may be discovered during a colonoscopy. Despite being visualized by colonoscopy, most of these polyps are generally referred for surgical resection. The risk of developing carcinoma in patients with appendiceal polyps is likely similar to that of other colonic polyps, so it is essential for the endoscopist to examine and visualize the appendiceal orifice thoroughly. Various techniques are available to the endoscopist that can increase the accuracy of colonoscopic evaluation. These include luminal inflation and deflation, looking behind and pressing haustral folds, and repetitive passage of the scope over poorly visualized areas. To our knowledge, only 3 cases have been reported in the literature describing the discovery of obscure appendiceal polyps using colonoscopic techniques. Here we describe three cases of appendiceal orifice polyps missed on initial visualization but subsequently protruded into the cecum following prolonged examination and gentle deflation in the cecum. The endoscopist should consider the possibility of an appendiceal neoplasm, especially if other colonic polyps have been found. Endoscopists should spend adequate time examining the cecum during a screening colonoscopy to expose and thoroughly examine the appendiceal region.

Intratumoral Cytotoxic T-Lymphocyte Density and PD-L1 Expression Are Prognostic Biomarkers for Patients with Colorectal Cancer

Background and objectives: Cytotoxic T-lymphocyte (CTL)-mediated inflammatory response to tumors plays a crucial role in preventing the progression of some cancers. Programmed cell death ligand 1 (PD-L1), a cell-surface glycoprotein, has been reported to repress T-cell-mediated immune responses against tumors. However, the clinical significance of PD-L1 in colorectal cancer (CRC) remains unclear. Our aim was to elucidate the prognostic significance of PD-L1 expression and CD8+ CTL density in CRC. Materials and methods: CD8 and PD-L1 immunostaining was conducted on 157 pathologic specimens from patients with CRC. The CD8+ CTL density and PD-L1 expression within the tumor microenvironment were assessed by immunohistochemistry. Results: Tumor invasion (pT) was significantly correlated with intratumoral (p = 0.011) and peritumoral (p = 0.016) CD8+ CTLs density in the tumor microenvironment. In addition, there was a significant difference in the intensity of CD8+ CTLs between patients with and without distant metastases (intratumoral p = 0.007; peritumoral p = 0.037, T-test). Lymph node metastasis (pN) and TNM stage were significantly correlated with PD-L1 expression in CRC cells (p = 0.015, p = 0.029, respectively). Multivariate analysis revealed a statistically significant relationship between the intratumoral CD8+ CTL density and disease-free survival (DFS) (hazard ratio [HR] 2.06; 95% confidence interval [CI]: 1.01–4.23; p = 0.043). The DFS was considerably shorter in patients with a high expression of PD-L1 in cancer cells than those with a low expression (univariate HR 2.55; 95% CI 1.50–4.34; p = 0.001; multivariate HR 0.48; 95% CI 0.28–0.82; p = 0.007). Conversely, patients with high PD-L1 expression in tumor-infiltrating lymphocytes had a longer DFS in both univariate analysis (HR 0.25; 95% CI: 0.14–0.44; p < 0.001) and multivariate analysis (HR 3.42; 95% CI: 1.95–6.01; p < 0.001). Conclusion: The CD8+ CTL density and PD-L1 expression are prognostic biomarkers for the survival of patients with CRC.