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Abstract Early diagnosis of breast cancer is exceptionally important in signifying the treatment results, of women’s health. The present study outlines a novel approach for analyzing breast cancer data by using the CatBoost classification model with a multi-layer perceptron neural network (CatBoost+MLP). Explainable artificial intelligence techniques are used to cohere with the proposed CatBoost with the MLP model. The proposed model aims to enhance the interpretability of predictions in breast cancer diagnosis by leveraging the benefits of CatBoost classification technique in feature identification and also contributing towards the interpretability of the decision model. The proposed CatBoost+MLP has been evaluated using the Shapley additive explanations values to analyze the feature significance in decision-making. Initially, the feature engineering is done using the analysis of variance technique to identify the significant features. The MLP model alone and the CatBoost+MLP model are being analyzed using divergent performance metrics, and the results obtained are compared with contemporary breast cancer identification techniques.

Abstract Cancer detection poses a significant challenge for researchers and clinical experts due to its status as the leading cause of global mortality. Early detection is crucial, but traditional cancer detection methods often rely on invasive procedures and time-consuming analyses, creating a demand for more efficient and accurate solutions. This paper addresses these challenges by utilizing automated cancer detection through AI-based techniques, specifically focusing on deep learning models. Convolutional Neural Networks (CNNs), including DenseNet121, DenseNet201, Xception, InceptionV3, MobileNetV2, NASNetLarge, NASNetMobile, InceptionResNetV2, VGG19, and ResNet152V2, are evaluated on image datasets for seven types of cancer: brain, oral, breast, kidney, Acute Lymphocytic Leukemia, lung and colon, and cervical cancer. Initially, images undergo segmentation techniques, proceeded by contour feature extraction where parameters such as perimeter, area, and epsilon are computed. The models are rigorously evaluated, with DenseNet121 achieving the highest validation accuracy as 99.94%, 0.0017 as loss, and the lowest Root Mean Square Error (RMSE) values as 0.036056 for training and 0.045826 for validation. These results revealed the capability of AI-based techniques in improving cancer detection accuracy, with DenseNet121 emerging as the most effective model in this study.

Abstract Background Bouveret syndrome is a rare etiology of gastric outlet obstruction, presenting with clinical manifestations that resemble those of several gastric pathologies. Timely diagnosis is imperative to mitigate potentially fatal consequences associated with this condition. Case presentation This case report involves a male patient, aged 35, who exhibited symptoms of intense abdominal discomfort, nausea, and vomiting over a 24-h period. The individual experienced identical symptoms of lesser intensity on three separate occasions throughout the preceding 4-month period, during which they resorted to analgesic medication for temporary relief. A contrast-enhanced computed tomography (CT) scan of the abdomen was performed, which identified the presence of the Rigler triad, characterized by pneumobilia, a cholecysto-antral fistula, and an ectopic gallstone lodged in the gastric antrum resulting in an obstruction. The patient’s diagnosis of Bouveret syndrome was determined based on the imaging findings. A surgical procedure known as cholecystectomy was conducted, which confirmed the existence of the sizable cholecystoduodenal fistula that was previously detected in the computed tomography (CT) scan. The surgical procedures that were conducted included a partial duodenectomy, a gastrojejunostomy, a cholecystectomy, and a common bile duct exploration with the removal of common bile duct stones. After examining the bile duct, a t-tube was inserted into the bile duct. Conclusion This case demonstrates a distinctive occurrence of a condition in a young man, typically observed in elderly females. There is a lack of established treatment guidelines for this particular entity. The appropriate course of action depends on various factors, including the patient’s condition, CT scan results, medical history, presence of co-existing illnesses, and intraoperative circumstances. These considerations are taken into account when determining whether a single-step surgical strategy or a multi-step approach should be pursued in order to minimize complications in patients. In this case, due to favorable conditions, a single-step approach was taken that yielded promising results.

Abstract In recent years, the growth spurt of medical imaging data has led to the development of various machine learning algorithms for various healthcare applications. The MedMNISTv2 dataset, a comprehensive benchmark for 2D biomedical image classification, encompasses diverse medical imaging modalities such as Fundus Camera, Breast Ultrasound, Colon Pathology, Blood Cell Microscope etc. Highly accurate classifications performed on these datasets is crucial for identification of various diseases and determining the course of treatment. This research paper presents a comprehensive analysis of four subsets within the MedMNISTv2 dataset: BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST. Each of these selected datasets is of diverse data modalities and comes with various sample sizes, and have been selected to analyze the efficiency of the model against diverse data modalities. The study explores the idea of assessing the Vision Transformer Model’s ability to capture intricate patterns and features crucial for these medical image classification and thereby transcend the benchmark metrics substantially. The methodology includes pre-processing the input images which is followed by training the ViT-base-patch16-224 model on the mentioned datasets. The performance of the model is assessed using key metrices and by comparing the classification accuracies achieved with the benchmark accuracies. With the assistance of ViT, the new benchmarks achieved for BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST are 97.90%, 90.38%, 94.62% and 57%, respectively. The study highlights the promise of Vision transformer models in medical image analysis, preparing the way for their adoption and further exploration in healthcare applications, aiming to enhance diagnostic accuracy and assist medical professionals in clinical decision-making.

The precise classification of medical images is crucial in various healthcare applications, especially in fields like disease diagnosis and treatment planning. In recent times, machine-intelligent models are desired to work in remote settings. However, the potential privacy concerns that arise from sharing confidential patient information to train traditional centralized machine learning models cannot be ignored. Federated learning (FL) offers a promising method for collaborative training on distributed data held by various entities, ensuring the privacy of patient information. This study evaluated the efficiency of the pre-trained models in the FL environment for medical image classification. The Convolutional Neural Network (CNN) model with Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP), along with the EfficientNet model, are being used as the local models. The trainable parameters from the local models are fed as input for building the global model. Pre-trained models trained on extensive datasets, possess valuable characteristics that can be utilized by FL models trained on proprietary datasets. Implementing this method can improve the efficacy and precision of FL models while also ensuring data confidentiality. The proposed model is evaluated using two distinct medical imaging datasets: Magnetic Resonance Image(MRI) and Computed Tomography (CT) scan images. The research highlights the advantages of utilizing pre-trained models in federated learning for medical image classification (MIC). The model’s performance is assessed across several assessment criteria, demonstrating the model exhibited a satisfactory accuracy rate of 97.4% and 98.8% for MRI and CT scan images, respectively. The model is evaluated concerning to Diagnostic Odds Ratio (DOR), where the proposed global model has exhibited 1164.54 for the MRI images and 6825.17 for CT scan images, and the values have outperformed the pretrained model.

IntroductionThe rapid advancement of science and technology has significantly expanded the capabilities of artificial intelligence, enhancing diagnostic accuracy for gastric cancer.MethodsThis research aims to utilize endoscopic images to identify various gastric disorders using an advanced Convolutional Neural Network (CNN) model. The Kvasir dataset, comprising images of normal Z-line, normal pylorus, ulcerative colitis, stool, and polyps, was used. Images were pre-processed and graphically analyzed to understand pixel intensity patterns, followed by feature extraction using adaptive thresholding and contour analysis for morphological values. Five deep transfer learning models—NASNetMobile, EfficientNetB5, EfficientNetB6, InceptionV3, DenseNet169—and a hybrid model combining EfficientNetB6 and DenseNet169 were evaluated using various performance metrics.Results & discussionFor the complete images of gastric cancer, EfficientNetB6 computed the top performance with 99.88% accuracy on a loss of 0.049. Additionally, InceptionV3 achieved the highest testing accuracy of 97.94% for detecting normal pylorus, while EfficientNetB6 excelled in detecting ulcerative colitis and normal Z-line with accuracies of 98.8% and 97.85%, respectively. EfficientNetB5 performed best for polyps and stool with accuracies of 98.40% and 96.86%, respectively.The study demonstrates that deep transfer learning techniques can effectively predict and classify different types of gastric cancer at early stages, aiding experts in diagnosis and detection.

Abstract Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.

Abstract Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by providing promising results in diagnosing, detecting, and classifying diseases. This paper explores the role of deep learning techniques in detecting and classifying various parasitic organisms. The research works on a dataset consisting of 34,298 samples of parasites such as Toxoplasma Gondii, Trypanosome, Plasmodium, Leishmania, Babesia, and Trichomonad along with host cells like red blood cells and white blood cells. These images are initially converted from RGB to grayscale followed by the computation of morphological features such as perimeter, height, area, and width. Later, Otsu thresholding and watershed techniques are applied to differentiate foreground from background and create markers on the images for the identification of regions of interest. Deep transfer learning models such as VGG19, InceptionV3, ResNet50V2, ResNet152V2, EfficientNetB3, EfficientNetB0, MobileNetV2, Xception, DenseNet169, and a hybrid model, InceptionResNetV2, are employed. The parameters of these models are fine-tuned using three optimizers: SGD, RMSprop, and Adam. Experimental results reveal that when RMSprop is applied, VGG19, InceptionV3, and EfficientNetB0 achieve the highest accuracy of 99.1% with a loss of 0.09. Similarly, using the SGD optimizer, InceptionV3 performs exceptionally well, achieving the highest accuracy of 99.91% with a loss of 0.98. Finally, applying the Adam optimizer, InceptionResNetV2 excels, achieving the highest accuracy of 99.96% with a loss of 0.13, outperforming other optimizers. The findings of this research signify that using deep learning models coupled with image processing methods generates a highly accurate and efficient way to detect and classify parasitic organisms.

Abstract Background The most prevalent cyanotic congenital heart disease is Tetralogy of Fallot (TOF). It has a variety of presentations and is made up of four anatomic abnormalities. Documented literature shows an incidence of 13–20% of a right aortic arch with an anomalous left subclavian artery among individuals diagnosed with TOF. This is the first case that discusses the rare occurrence of overriding of the aortic arch along with the left aberrant subclavian artery and vertebral defect in a 3-week-old girl. Timely identification and management are pivotal in ensuring the best possible outcomes for these young patients. Case presentation A 3-week-old female child came with complaints of dyspnea, dysphagia, fatigue, and cyanosis on extreme crying, feeding, and moderate activity. Echocardiography revealed severe pulmonary stenosis with right ventricular dilatation and ventricular septal defect (VSD); a chest computed tomography was performed that revealed four characteristic features of TOF (pulmonary artery stenosis, VSD, right aortic root deviation, and concentric right ventricular hypertrophy) along with overriding of the aortic arch accompanied with the left aberrant subclavian artery (compressing the trachea and infundibulum) and vertebral defect (butterfly vertebra). Conclusions The case of this 3-week-old female infant emphasizes the importance of early and accurate diagnosis in congenital heart diseases, particularly when faced with complex presentations such as the TOF. It serves as a testament to the valuable role of advanced diagnostic imaging techniques in unraveling the complexity of congenital heart conditions.

Background: Psoriasis Vulgaris (PsV) is one of the most severe chronic, immune mediated skin diseases. The Notch signaling pathway, a key regulator for epidermal renewal, contributes in differentiation, proliferation and survival of keratinocytes. Alterations in the NOTCH4 gene disrupts Notch pathway. The current study aimed to find out the association of NOTCH4 gene polymorphism(s) in Pakistani psoriatic patients. Methods: A case control study, 390 DNA samples (190 samples of Psoriasis Vulgaris and 200 healthy control individuals), from January-December 2019, were selected from Rawalpindi Leprosy Hospital and healthy population, respectively. For amplification of (rs387071) SNP of NOTCH4 gene, lab standard protocols for T-ARMS-PCR were followed. Frequencies of genotype and allele were calculated by using Hardy Weinberg theorem. Data were analyzed by using SPSS and p-value ≤0.05 was considered statistically significant. Results: The mean age of patients was 34.6±14 while among control subjects it was 32.8±10.0. In cases, genotype homozygous A/A119 (62.6%) was more prevalent, followed by heterozygous A/G171 (90%) while homozygous G/G19 (10%) was the least prevalent between cases and controls. Allele A frequency in diseased subjects was 0.76 while, for controls it was 0.78. In addition, allele G frequency in patients and controls was 0.24 and 0.22 respectively. Based on Hardy Weinberg equilibrium, no association of (rs387071) NOTCH4 gene with psoriasis cases was found. Conclusion: NOTCH4 gene (rs387071) polymorphism was not significantly associated with patients of psoriasis Vulgaris in Pakistan. Larger studies are required to establish ethnic-specific markers for psoriasis in Pakistani population. Keywords: Psoriasis Vulgaris; PCR; NOTCH4; Polymorphism.

Dear Editor First identified in Wuhan, China, on December 31 2019, coronavirus spread like bush fire. The WHO declared it as a Global Pandemic on March 11 2021. COVID-19 was termed novel based on newly identified pathogens, thus leading to a state of uncertainty regarding treatment and prevention1. This virus can present with an array of symptoms; dry cough, breathlessness, chest pain, myalgia, and diarrhea2. Undergraduate medical education has shifted to virtual learning systems to halt the transmission of the virus. This letter elaborates on the challenges faced and the impact of COVID-19 on the life of undergraduate medical students.

Background: Even though organ transplants save thousands of lives globally, there exists a wide mismatch between an increasing demand for organ transplants and limited number of organ donors. The aim of the study was to assess the reasons and inspect differences between donors and non-donors in both students of medical and non-medical universities. Methods: A cross-sectional study was conducted over a period of 6 months (May 2018-March 2021). Sample of 400 participants enrolled from three medical colleges, two business/social sciences institutions and one engineering university of Karachi. Categorical variables were compared using the Chi-squared test. Continuous variables were compared using a student t-test. Variables with a univariate p-value ≤ 0.10 were included in multiple logistic regression models. Adjusted odds ratio (OR) with 95% confidence interval were reported. Results: With a very high response rate 383(96%) for knowledge about organ donation, a subdued response 201(52.3%) for willingness to donate. Most common source of knowledge regarding organ donation was social media 249(65%), followed by university 207(54%), family/friends 184(48%) and television 172(45%). Multiple logistic regression model identified being comfortable discussing organ donation as a positive predictor of willingness to donate (OR: 2.71, p=0.005), and the belief that organ donation disfigures the body as a negative predictor (OR: 0.40, p

Abstract In this article, a low-complexity VLSI architecture based on a radix-4 hyperbolic COordinate Rotion DIgital Computer (CORDIC) is proposed to compute the $$N{{\rm th}}$$ N th root and $$N{{\rm th}}$$ N th power of a fixed-point number. The most recent techniques use the radix-2 CORDIC algorithm to compute the root and power. The high computation latency of radix-2 CORDIC is the primary concern for the designers. $$N{{\rm th}}$$ N th root and $$N{{\rm th}}$$ N th power computations are divided into three phases, and each phase is performed by a different class of the proposed modified radix-4 CORDIC algorithms in the proposed architecture. Although radix-4 CORDIC can converge faster with fewer recurrences, it demands more hardware resources and computational steps due to its intricate angle selection logic and variable scale factor. We have employed the modified radix-4 hyperbolic vectoring (R4HV) CORDIC to compute logarithms, radix-4 linear vectoring (R4LV) to perform division, and the modified scaling-free radix-4 hyperbolic rotation (R4HR) CORDIC to compute exponential. The criteria to select the amount of rotation in R4HV CORDIC is complicated and depends on the coordinates $$X^j$$ X j and $$Y^j$$ Y j of the rotating vector. In the proposed modified R4HV CORDIC, we have derived the simple selection criteria based on the fact that the inputs to R4HV CORDIC are related. The proposed criteria only depend on the coordinate $$Y^j$$ Y j that reduces the hardware complexity of the R4HV CORDIC. The R4HR CORDIC shows the complex scale factor, and compensation of such scale factor necessitates the complex hardware. The complexity of R4HR CORDIC is reduced by pre-computing the scale factor for initial iterations and by employing scaling-free rotations for later iterations. Quantitative hardware analysis suggests better hardware utilization than the recent approaches. The proposed architecture is implemented on a Virtex-6 FPGA, and FPGA implementation demonstrates $$19\%$$ 19 % less hardware utilization with better error performance than the approach with the radix-2 CORDIC algorithm.

Abstract Agriculture plays a pivotal role in the economies of developing countries by providing livelihoods, sustenance, and employment opportunities in rural areas. However, crop diseases pose a significant threat to both farmers’ incomes and food security. Furthermore, these diseases also show adverse effects on human health by causing various illnesses. Till date, only a limited number of studies have been conducted to identify and classify diseased cauliflower plants but they also face certain challenges such as insufficient disease surveillance mechanisms, the lack of comprehensive datasets that are properly labelled as well as are of high quality, and the considerable computational resources that are necessary for conducting thorough analysis. In view of the aforementioned challenges, the primary objective of this manuscript is to tackle these significant concerns and enhance understanding regarding the significance of cauliflower disease identification and detection in rural agriculture through the use of advanced deep transfer learning techniques. The work is conducted on the four classes of cauliflower diseases i.e. Bacterial spot rot, Black rot, Downy Mildew, and No disease which are taken from VegNet dataset. Ten deep transfer learning models such as EfficientNetB0, Xception, EfficientNetB1, MobileNetV2, EfficientNetB2, DenseNet201, EfficientNetB3, InceptionResNetV2, EfficientNetB4, and ResNet152V2, are trained and examined on the basis of root mean square error, recall, precision, F1-score, accuracy, and loss. Remarkably, EfficientNetB1 achieved the highest validation accuracy (99.90%), lowest loss (0.16), and root mean square error (0.40) during experimentation. It has been observed that our research highlights the critical role of advanced CNN models in automating cauliflower disease detection and classification and such models can lead to robust applications for cauliflower disease management in agriculture, ultimately benefiting both farmers and consumers.

The emergence of federated learning (FL) technique in fog-enabled healthcare system has leveraged enhanced privacy towards safeguarding sensitive patient information over heterogeneous computing platforms. In this paper, we introduce the FedHealthFog framework, which was meticulously developed to overcome the difficulties of distributed learning in resource-constrained IoT-enabled healthcare systems, particularly those sensitive to delays and energy efficiency. Conventional federated learning approaches face challenges stemming from substantial compute requirements and significant communication costs. This is primarily due to their reliance on a singular server for the aggregation of global data, which results in inefficient training models. We present a transformational approach to address these problems by elevating strategically placed fog nodes to the position of local aggregators within the federated learning architecture. A sophisticated greedy heuristic technique is used to optimize the choice of a fog node as the global aggregator in each communication cycle between edge devices and the cloud. The FedHealthFog system notably accounts for drop in communication latency of 87.01%, 26.90%, and 71.74%, and energy consumption of 57.98%, 34.36%, and 35.37% respectively, for three benchmark algorithms analyzed in this study. The effectiveness of FedHealthFog is strongly supported by outcomes of our experiments compared to cutting-edge alternatives while simultaneously reducing number of global aggregation cycles. These findings highlight FedHealthFog's potential to transform federated learning in resource-constrained IoT environments for delay-sensitive applications.

Abstract Colorectal cancer is the third most common type of cancer diagnosed annually, and the second leading cause of death due to cancer. Early diagnosis of this ailment is vital for preventing the tumours to spread and plan treatment to possibly eradicate the disease. However, population-wide screening is stunted by the requirement of medical professionals to analyse histological slides manually. Thus, an automated computer-aided detection (CAD) framework based on deep learning is proposed in this research that uses histological slide images for predictions. Ensemble learning is a popular strategy for fusing the salient properties of several models to make the final predictions. However, such frameworks are computationally costly since it requires the training of multiple base learners. Instead, in this study, we adopt a snapshot ensemble method, wherein, instead of the traditional method of fusing decision scores from the snapshots of a Convolutional Neural Network (CNN) model, we extract deep features from the penultimate layer of the CNN model. Since the deep features are extracted from the same CNN model but for different learning environments, there may be redundancy in the feature set. To alleviate this, the features are fed into Particle Swarm Optimization, a popular meta-heuristic, for dimensionality reduction of the feature space and better classification. Upon evaluation on a publicly available colorectal cancer histology dataset using a five-fold cross-validation scheme, the proposed method obtains a highest accuracy of 97.60% and F1-Score of 97.61%, outperforming existing state-of-the-art methods on the same dataset. Further, qualitative investigation of class activation maps provide visual explainability to medical practitioners, as well as justifies the use of the CAD framework in screening of colorectal histology. Our source codes are publicly accessible at: https://github.com/soumitri2001/SnapEnsemFS .

Abstract Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in the field of Industry 4.0. But there exist severe challenges related to data privacy and security when processing the automatic and practical data collection and monitoring over industrial applications in IIoT. Traditional user authentication strategies in IIoT are affected by single factor authentication, which leads to poor adaptability along with the increasing users count and different user categories. For addressing such issue, this paper aims to implement the privacy preservation model in IIoT using the advancements of artificial intelligent techniques. The two major stages of the designed system are the sanitization and restoration of IIoT data. Data sanitization hides the sensitive information in IIoT for preventing it from leakage of information. Moreover, the designed sanitization procedure performs the optimal key generation by a new Grasshopper–Black Hole Optimization (G–BHO) algorithm. A multi-objective function involving the parameters like degree of modification, hiding rate, correlation coefficient between the actual data and restored data, and information preservation rate was derived and utilized for generating optimal key. The simulation result establishes the dominance of the proposed model over other state-of the-art models in terms of various performance metrics. In respect of privacy preservation, the proposed G–BHO algorithm has achieved 1%, 15.2%, 12.6%, and 1% enhanced result than JA, GWO, GOA, and BHO, respectively.

Background and Aims: The 2012 and 2020 US Multi-Society Task Force postpolypectomy guidelines have recommended progressively longer surveillance intervals for patients with low-risk adenomas (LRAs). These guidelines require data from past colonoscopies. We examined the impact of the 2012 guidelines for second surveillance on clinical practice, including the availability of prior colonoscopy data, with the aim of informing the implementation of the 2020 guidelines. Methods: We identified surveillance colonoscopies at Stanford Health Care and the Palo Alto Veterans Affairs Health Care System in 3 periods: preguideline (March–August 2012), postguideline (January–June 2013), and delayed postguideline (July–September 2017). We collected data on the most recent previous colonoscopy, findings at the study entry surveillance colonoscopy, and recommendations for subsequent surveillance. Results: Among 977 patients, the most recent prior colonoscopy data were available in 78% of preguideline, 78% of postguideline, and 61% of delayed postguideline cases (P < .001). The fraction of surveillance colonoscopy reports that deferred recommendations awaiting pathology increased from 6% to 11% in preguideline and postguideline to 59% in delayed postguideline cases (P < .001). Overall adherence to guidelines for subsequent surveillance was similar in all 3 periods (54%–67%; P = .089). In the postguideline and delayed postguideline periods combined, a 10-year subsequent surveillance interval was recommended in 0 of 29 cases with LRA followed by normal surveillance colonoscopy. Conclusion: In patients undergoing surveillance, prior colonoscopy data were not always available and recommendations were often deferred awaiting pathology. Adherence to subsequent surveillance guidelines was suboptimal, especially for LRA followed by normal colonoscopy. Strategies addressing these gaps are needed to optimize implementation of the updated 2020 postpolypectomy guidelines.