Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

Automatic Fetal Segmentation Designed on Computer-Aided Detection with Ultrasound Images

In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be utilized toward determining gestational age and tracking fetal development.This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited.The CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull.We identified the HC using dynamic programming,an elliptical fit,and a Hough transform.The computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test set.We used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,respectively.The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of days.The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 days.The outcomes reveal that the computer-aided detection(CAD)program outperforms an expert sonographer.When paired with the classifications reported in the literature,the provided system achieves results that are comparable or even better.We have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.

Fault diagnosis method of link control system for gravitational wave detection

To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Different from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling nonlinearity.By constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault parameters.The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.

Early diagnosis of esophageal cancer:How to put“early detection”into effect?

This editorial comments on the article by Qu et al in a recent edition of World Journal of Gastrointestinal Oncology,focusing on the importance of early diagnosis in managing esophageal cancer and strategies for achieving“early detection”.The five-year age-standardized net survival for esophageal cancer patients falls short of expectations.Early detection and accurate diagnosis are critical strategies for improving the treatment outcomes of esophageal cancer.While advancements in endoscopic technology have been significant,there seems to be an excessive emphasis on the latest high-end endoscopic devices and various endoscopic resection techniques.Therefore,it is imperative to redirect focus towards proactive early detection strategies for esophageal cancer,investigate the most cost-effective screening methods suitable for different regions,and persistently explore practical solutions to improve the five-year survival rate of patients with esophageal cancer.

Automation in road distress detection,diagnosis and treatment

Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the disease.This study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt roads.Specifically,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared.In addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack sealing.Despite their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time performance.Consequently,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and treatment.This paper addresses the challenges of precise defect detection,condition assessment,and unmanned construction.At the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures

Deep neural network(DNN)based computer-aided breast tumor diagnosis(CABTD)method plays a vital role in the early detection and diagnosis of breast tumors.However,a Brightness mode(B-mode)ultrasound image derives training feature samples that make closer isolation toward the infection part.Hence,it is expensive due to a metaheuristic search of features occupying the global region of interest(ROI)structures of input images.Thus,it may lead to the high computational complexity of the pre-trained DNN-based CABTD method.This paper proposes a novel ensemble pretrained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images.It conveys the additional consideration of a local-ROI-structures for further enhan-cing the pretrained DNN-based CABTD method’s breast tumor diagnostic performance without degrading its visual quality.The features are extracted at various depths(18,50,and 101)from the global and local ROI structures and feed to support vector machine for better classification.From the experimental results,it has been observed that the combined local and global ROI structure of small depth residual network ResNet18(0.8 in%)has produced significant improve-ment in pixel ratio as compared to ResNet50(0.5 in%)and ResNet101(0.3 in%),respectively.Subsequently,the pretrained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors(Benign and Malignant)and improve the diagnostic accuracy(86%)compared to Dense Net,Alex Net,VGG Net,and Google Net.Moreover,it reduces the computational complexity due to the small depth residual network ResNet18,respectively.

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art performance.However,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models fail.In this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called SAEU.In SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks.Based on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault diagnosis.Moreover,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability analy-sis.Finally,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

Development of RPA-Cas12a-fluorescence assay for rapid and reliable detection of human bocavirus 1

Human bocavirus(HBoV)1 is considered an important pathogen that mainly affects infants aged 6–24 months,but preventing viral transmission in resource-limited regions through rapid and affordable on-site diagnosis of individuals with early infection of HBoV1 remains somewhat challenging.Herein,we present a novel faster,lower cost,reliable method for the detection of HBoV1,which integrates a recombinase polymerase amplification(RPA)assay with the CRISPR/Cas12a system,designated the RPA-Cas12a-fluorescence assay.The RPA-Cas12a-fluorescence system can specifically detect target gene levels as low as 0.5 copies of HBoV1 plasmid DNA per microliter within 40 min at 37℃without the need for sophisticated instruments.The method also demonstrates excellent specificity without cross-reactivity to non-target pathogens.Furthermore,the method was appraised using 28 clinical samples,and displayed high accuracy with positive and negative predictive agreement of 90.9%and 100%,respectively.Therefore,our proposed rapid and sensitive HBoV1 detection method,the RPA-Cas12a-fluorescence assay,shows promising potential for early on-site diagnosis of HBoV1 infection in the fields of public health and health care.The established RPA-Cas12a-fluorescence assay is rapid and reliable method for human bocavirus 1 detection.The RPA-Cas12a-fluorescence assay can be completed within 40 min with robust specificity and sensitivity of 0.5 copies/μl.

Recent advances in living cell nucleic acid probes based on nanomaterials for early cancer diagnosis

The early diagnosis of cancer is vital for effective treatment and improved prognosis. Tumor biomarkers, which can be used for the early diagnosis, treatment, and prognostic evaluation of cancer, have emerged as a topic of intense research interest in recent years. Nucleic acid, as a type of tumor biomarker, contains vital genetic information, which is of great significance for the occurrence and development of cancer. Currently, living cell nucleic acid probes, which enable the in situ imaging and dynamic monitoring of nucleic acids, have become a rapidly developing field. This review focuses on living cell nucleic acid probes that can be used for the early diagnosis of tumors. We describe the fundamental design of the probe in terms of three units and focus on the roles of different nanomaterials in probe delivery.

Improving early diagnosis of multiple endocrine neoplasia type 1 by assessing the gastrointestinal symptoms,hypercalcemia,and elevated serum gastrin

Despite advancements in the field,early diagnosis of multiple endocrine neoplasia type 1(MEN1)remains unachievable.This letter to the editor highlighted the importance of carefully assessing gastrointestinal symptoms,hypercalcemia,and elevated serum gastrin levels,as suggested by Yuan et al in their paper.They focused on a patient with recurrent abdominal pain and diarrhea whose diagnostic path led to establishing a MEN1 diagnosis within a year.This emphasized the need for clinicians to consider MEN1 in patients with similar presentations,particularly when gastrointestinal symptoms persist or recur after discontinuation of proton pump inhibitors,especially knowing that early recognition and intervention are crucial for improving patient outcomes.

Expediting carbon dots synthesis by the active adaptive method with machine learning and applications in dental diagnosis and treatment

Synthesis of functional nanostructures with the least number of tests is paramount towards the propelling materials development. However, the synthesis method containing multivariable leads to high uncertainty, exhaustive attempts, and exorbitant manpower costs. Machine learning (ML) burgeons and provokes an interest in rationally designing and synthesizing materials. Here, we collect the dataset of nano-functional materials carbon dots (CDs) on synthetic parameters and optical properties. ML is applied to assist the synthesis process to enhance photoluminescence quantum yield (QY) by building the methodology named active adaptive method (AAM), including the model selection, max points screen, and experimental verification. An interactive iteration strategy is the first time considered in AAM with the constant acquisition of the furnished data by itself to perfect the model. CDs exhibit a strong red emission with QY up to 23.3% and enhancement of around 200% compared with the pristine value obtained through the AAM guidance. Furthermore, the guided CDs are applied as metal ions probes for Co^(2+) and Fe^(3+), with a concentration range of 0–120 and 0–150 µM, and their detection limits are 1.17 and 0.06 µM. Moreover, we also apply CDs for dental diagnosis and treatment using excellent optical ability. It can effectively detect early caries and treat mineralization combined with gel. The study shows that the error of experiment verification gradually decreases and QY improves double with the effective feedback loops by AAM, suggesting the great potential of utilizing ML to guide the synthesis of novel materials. Finally, the code is open-source and provided to be referenced for further investigation on the novel inorganic material prediction.

Endoscopic detection and diagnostic strategies for minute gastric cancer:A real-world observational study

BACKGROUND Minute gastric cancers(MGCs)have a favorable prognosis,but they are too small to be detected by endoscopy,with a maximum diameter≤5 mm.AIM To explore endoscopic detection and diagnostic strategies for MGCs.METHODS This was a real-world observational study.The endoscopic and clinicopathological parameters of 191 MGCs between January 2015 and December 2022 were retrospectively analyzed.Endoscopic discoverable opportunity and typical neoplastic features were emphatically reviewed.RESULTS All MGCs in our study were of a single pathological type,97.38%(186/191)of which were differentiated-type tumors.White light endoscopy(WLE)detected 84.29%(161/191)of MGCs,and the most common morphology of MGCs found by WLE was protruding.Narrow-band imaging(NBI)secondary observation detected 14.14%(27/191)of MGCs,and the most common morphology of MGCs found by NBI was flat.Another three MGCs were detected by indigo carmine third observation.If a well-demarcated border lesion exhibited a typical neoplastic color,such as yellowish-red or whitish under WLE and brownish under NBI,MGCs should be diagnosed.The proportion with high diagnostic confidence by magnifying endoscopy with NBI(ME-NBI)was significantly higher than the proportion with low diagnostic confidence and the only visible groups(94.19%>56.92%>32.50%,P<0.001).CONCLUSION WLE combined with NBI and indigo carmine are helpful for detection of MGCs.A clear demarcation line combined with a typical neoplastic color using nonmagnifying observation is sufficient for diagnosis of MGCs.MENBI improves the endoscopic diagnostic confidence of MGCs.

Automatic detection of breast lesions in automated 3D breast ultrasound with cross-organ transfer learning

Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.

Clinical Application of Sex Hormone in Different Physiological Periods in the Diagnosis of Infertility Patients

Background: Infertility is characterized by the inability to conceive after a year of regular unprotected intercourse. Aims: This study aimed to investigate the diagnostic value of sex hormone levels during different physiological periods in the diagnosis of infertility patients. Methods: From December 2019 to May 2021, a total of 93 infertility patients were admitted and selected as the observation group. Among them, 31 cases were in the follicular stage, 31 cases in the ovulation stage, and 31 cases in the luteal stage. Ninety-three healthy women for fertility evaluation due to male infertility were selected as the control group. The control group included 31 women in the follicular phase, 31 women in the ovulatory phase, and 31 women in the luteal phase. The levels of sex hormones (prolactin (PRL), luteinizing hormone (LH), follicle-stimulating hormone (FSH), estradiol (E2), testosterone (T), and progesterone (P)) during different physiological phases were compared between the observation and control groups. Results: The follicular phase showed no significant difference in LH levels between the observation group and the control group. The observation group showed higher levels of PRL and P compared to the control group, while the levels of FSH, E2, and T were lower in the observation group compared to the control group. The ovulation phase showed no significant difference in PRL levels between the two groups. The observation group showed lower levels of LH, FSH, E2, T, and P compared to the control group. The luteal phase showed no statistical difference in E2 levels between the two groups. The observation group showed higher levels of PRL, LH, and FSH compared to the control group, while the levels of T and P were lower in the observation group compared to the control group. Conclusion: Infertile women show variations in hormone levels compared to the normal levels during the follicular phase, ovulatory phase, and luteal phase.

Study on the correlation between abdominal infection and psychological stress in children based on nucleic acid detection

BACKGROUND Diagnosing and treating abdominal infection in children remains a challenge.Nucleic acid detection,as a rapid and accurate diagnosis tool,has great significance in this field.AIM To investigate the diagnosis and treatment of abdominal infection by nucleic acid detection and its possible correlation with psychological stress in children.METHODS A total of 50 pediatric patients diagnosed with abdominal infections between September 2020 and July 2021 were included in this study.Intra-abdominal pus samples were collected for pathogen culture,drug susceptibility testing,and broad-spectrum bacterial nucleic acid testing.Psychological stress,anxiety,depression,and coping styles were assessed using the coping with a disease(CODI)scale.RESULTS Based on susceptibility testing,a regimen of cefazoxime,piperacillin/tazobactam,and metronidazole or ornidazole achieved 100%effectiveness in treating appendicitis.Psychological assessments revealed a positive correlation between pressure level and both anxiety(r=0.324,P=0.001)and depressive disorders(r=0.325,P<0.001).Acceptance and distancing as coping strategies were negatively correlated with anxiety and depression,while negative emotional responses were strongly associated with increased anxiety(r=0.574,P<0.001)and depression(r=0.511,P=0.001).Coping strategies such as illusion and escape showed no significant correlation with emotional outcomes.CONCLUSION Nucleic acid testing helps in the diagnosis of abdominal infections in children,and also focuses on children's mental health.

Advancing Early Detection of Colorectal Adenomatous Polyps via Genetic Data Analysis: A Hybrid Machine Learning Approach

In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.

Advancements and Challenges in Biomarkers for Colorectal Cancer Detection:A Comprehensive Review

This study provides an overview of the current landscape of biomarkers for colorectal cancer(CRC)detection,focusing on genetic,proteomic,circulating microRNA(miRNA),and metabolomic biomarkers.CRC remains a significant global health challenge,ranking among the most prevalent cancers worldwide and being a leading cause of cancer-related deaths.Despite advancements in screening methods such as colonoscopy,sigmoidoscopy,and fecal occult blood tests(FOBT),the asymptomatic nature of early-stage CRC often results in late diagnoses,negatively impacting patient outcomes.Genetic biomarkers like APC,KRAS,TP53,and microsatellite instability(MSI)play critical roles in CRC pathogenesis and progression.These biomarkers,detectable through polymerase chain reaction,next-generation sequencing,and other advanced techniques,guide early detection and personalized treatment decisions.Proteomic biomarkers such as CEA,CA 19-9,and novel signatures offer insights into CRC’s physiological changes and disease status,aiding prognosis and treatment response assessments through enzyme-linked immunosorbent assay and mass spectrometry.Circulating miRNAs,including miR-21 and miR-92a,present promising non-invasive biomarkers that can be detected in blood and stool samples,reflecting CRC presence,progression,and therapeutic response.Metabolomic biomarkers,encompassing amino acids,lipids,and TCA cycle intermediates,provide further insights into CRC-associated metabolic alterations,which are crucial for early detection and treatment monitoring using mass spectrometry and nuclear magnetic resonance.Despite these advancements,challenges such as biomarker validation,standardization,and clinical utility remain.Future research directions include integrating multi-omics approaches and leveraging technologies like liquid biopsies and AI for enhanced biomarker discovery and clinical application.By addressing these challenges and advancing research in biomarker development,CRC screening and management could potentially be revolutionized,improving patient outcomes and reducing the global burden of this disease.