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.

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.

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.

New direction for surgery:Super minimally invasive surgery

The top goal of modern medicine is treating disease without destroying organ structures and making patients as healthy as they were before their sickness.Minimally invasive surgery(MIS)has dominated the surgical realm because of its lesser invasiveness.However,changes in anatomical structures of the body and reconstruction of internal organs or different organs are common after traditional surgery or MIS,decreasing the quality of life of patients post-operation.Thus,I propose a new treatment mode,super MIS(SMIS),which is defined as“curing a disease or lesion which used to be treated by MIS while preserving the integrity of the organs”.In this study,I describe the origin,definition,operative channels,advantages,and future perspectives of SMIS.

Bariatric surgery and diabetes:Current challenges and perspectives

Diabetes mellitus(DM)and obesity have become public issues of global concern.Bariatric surgery for the treatment of obesity combined with type 2 DM has been shown to be a safe and effective approach;however,there are limited studies that have systematically addressed the challenges of surgical treatment of obesity combined with DM.In this review,we summarize and answer the most pressing questions in the field of surgical treatment of obesity-associated DM.I believe that our insights will be of great help to clinicians in their daily practice.

Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images

Accurate segmentation of oral surgery-related tissues from cone beam computed tomography(CBCT)images can significantly accelerate treatment planning and improve surgical accuracy.In this paper,we propose a fully automated tissue segmentation system for dental implant surgery.Specifically,we propose an image preprocessing method based on data distribution histograms,which can adaptively process CBCT images with different parameters.Based on this,we use the bone segmentation network to obtain the segmentation results of alveolar bone,teeth,and maxillary sinus.We use the tooth and mandibular regions as the ROI regions of tooth segmentation and mandibular nerve tube segmentation to achieve the corresponding tasks.The tooth segmentation results can obtain the order information of the dentition.The corresponding experimental results show that our method can achieve higher segmentation accuracy and efficiency compared to existing methods.Its average Dice scores on the tooth,alveolar bone,maxillary sinus,and mandibular canal segmentation tasks were 96.5%,95.4%,93.6%,and 94.8%,respectively.These results demonstrate that it can accelerate the development of digital dentistry.

Is recovery enhancement after gastric cancer surgery really a safe approach for elderly patients?

BACKGROUND This study aimed to evaluate the safety of enhanced recovery after surgery(ERAS)in elderly patients with gastric cancer(GC).AIM To evaluate the safety of ERAS in elderly patients with GC.METHODS The PubMed,EMBASE,and Cochrane Library databases were used to search for eligible studies from inception to April 1,2023.The mean difference(MD),odds ratio(OR)and 95%confidence interval(95%CI)were pooled for analysis.The quality of the included studies was evaluated using the Newcastle-Ottawa Scale scores.We used Stata(V.16.0)software for data analysis.RESULTS This study consists of six studies involving 878 elderly patients.By analyzing the clinical outcomes,we found that the ERAS group had shorter postoperative hospital stays(MD=-0.51,I2=0.00%,95%CI=-0.72 to-0.30,P=0.00);earlier times to first flatus(defecation;MD=-0.30,I²=0.00%,95%CI=-0.55 to-0.06,P=0.02);less intestinal obstruction(OR=3.24,I2=0.00%,95%CI=1.07 to 9.78,P=0.04);less nausea and vomiting(OR=4.07,I2=0.00%,95%CI=1.29 to 12.84,P=0.02);and less gastric retention(OR=5.69,I2=2.46%,95%CI=2.00 to 16.20,P=0.00).Our results showed that the conventional group had a greater mortality rate than the ERAS group(OR=0.24,I2=0.00%,95%CI=0.07 to 0.84,P=0.03).However,there was no statistically significant difference in major complications between the ERAS group and the conventional group(OR=0.67,I2=0.00%,95%CI=0.38 to 1.18,P=0.16).CONCLUSION Compared to those with conventional recovery,elderly GC patients who received the ERAS protocol after surgery had a lower risk of mortality.

Enhanced recovery after surgery in elderly patients with non-small cell lung cancer who underwent video-assisted thoracic surgery

BACKGROUND This study was designed to investigate the clinical outcomes of enhanced recovery after surgery(ERAS)in the perioperative period in elderly patients with nonsmall cell lung cancer(NSCLC).AIM To investigate the potential enhancement of video-assisted thoracic surgery(VATS)in postoperative recovery in elderly patients with NSCLC.METHODS We retrospectively analysed the clinical data of 85 elderly NSCLC patients who underwent ERAS(the ERAS group)and 327 elderly NSCLC patients who received routine care(the control group)after VATS at the Department of Thoracic Surgery of Peking University Shenzhen Hospital between May 2015 and April 2017.After propensity score matching of baseline data,we analysed the postoperative stay,total hospital expenses,postoperative 48-h pain score,and postoperative complication rate for the 2 groups of patients who underwent lobectomy or sublobar resection.RESULTS After propensity score matching,ERAS significantly reduced the postoperative hospital stay(6.96±4.16 vs 8.48±4.18 d,P=0.001)and total hospital expenses(48875.27±18437.5 vs 55497.64±21168.63 CNY,P=0.014)and improved the satisfaction score(79.8±7.55 vs 77.35±7.72,P=0.029)relative to those for routine care.No significant between-group difference was observed in postoperative 48-h pain score(4.68±1.69 vs 5.28±2.1,P=0.090)or postoperative complication rate(21.2%vs 27.1%,P=0.371).Subgroup analysis showed that ERAS significantly reduced the postoperative hospital stay and total hospital expenses and increased the satisfaction score of patients who underwent lobectomy but not of patients who underwent sublobar resection.CONCLUSION ERAS effectively reduced the postoperative hospital stay and total hospital expenses and improved the satisfaction score in the perioperative period for elderly NSCLC patients who underwent lobectomy but not for patients who underwent sublobar resection.

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.

Glaucoma surgery experiments using digital microscope-integrated optical coherence tomography and OCT-compatible instruments

There is a certain failure rate in traditional glaucoma surgery because of the lack of depth information in microscope images.In this work,we present a digital microscope-integrated optical coherence tomography(MIOCT)system and several custom-made OCT-compatible instruments for glaucoma surgery.Sixteen ophthalmologists were asked to perform trabeculectomy and canaloplasty on live porcine eyes using the system and instruments.After surgery,a subjective feedback survey about the user experience was taken.The experiment results showed that our system can help surgeons easily locate important tissue structures during surgery.The custom-made instruments also solved the shadowing problem in OCT imaging.Surgeons preferred to use the system in their future practice.

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.

Uterine artery pseudoaneurysm caused by hysteroscopic surgery: A case report

BACKGROUND We report a case of uterine artery pseudoaneurysm(UAP)occurrence during hysteroscopic endometrial polypectomy and its treatment via uterine artery embolization(UAE).CASE SUMMARY A 48-year-old primigravid,primiparous patient was incidentally found to have an endometrial polyp during a health checkup,and underwent a hysteroscopic polypectomy at another hospital.Her cervix was dilated with a Laminken-R®device.After the Laminken-R®was withdrawn,a large amount of genital bleeding was observed.This bleeding persisted after the hysteroscopic polypectomy,and,as hemostasis became impossible,the patient was transferred to our hospital by ambulance.On arrival,transvaginal ultrasonography revealed a 3-cm hypoechoic mass with a swirling internal pulse on the right side of the uterus,and color Doppler ultrasonography showed feeder vessels penetrating the mass.Pelvic contrast-enhanced computed tomography(CT)confirmed the presence of a mass at this site,and vascular proliferation was observed within the uterine cavity.Consequently,UAP was diagnosed,and UAE was performed.The patient’s postoperative course was uneventful,and 6 mo post-UAE,no recurrence of blood flow to the UAP was observed.CONCLUSION When abnormal genital bleeding occurs during hysteroscopic surgery,ultrasonography and contrast-enhanced CT can assist in the detection of early UAPs.

Effect of bariatric surgery on metabolism in diabetes and obesity comorbidity:Insight from recent research

Obesity is a prevalent cause of diabetes mellitus(DM)and is a serious danger to human health.Type 2 DM(T2DM)mostly occurs along with obesity.Foodborne obesity-induced DM is caused by an excessive long-term diet and surplus energy.Bariatric surgery can improve the symptoms of T2DM in some obese patients.But different types of bariatric surgery may have different effects.There are some models built by researchers to discuss the surgical procedures’effects on me-tabolism in diabetes animal models and diabetes patients.It is high time to conclude all this effects and recommend procedures that can better improve metabolism.

Treatment of hemolymphangioma by robotic surgery: A case report

BACKGROUND Hemolymphangioma of the jejunum is rare and lacks clinical specificity,and can manifest as gastrointestinal bleeding,abdominal pain,and intestinal obstruction.Computed tomography,magnetic resonance imaging,and other examinations show certain characteristics of the disease,but lack accuracy.Although capsule endoscopy and enteroscopy make up for this deficiency,the diagnosis also still re-quires pathology.CASE SUMMARY A male patient was admitted to the hospital due to abdominal distension and abdominal pain,but a specific diagnosis by computed tomography examination was not obtained.Partial resection of the small intestine was performed by robotic surgery,and postoperative pathological biopsy confirmed the diagnosis of hemo-lymphangioma.No recurrence in the follow-up examination was observed.CONCLUSION Robotic surgery is an effective way to treat hemolymphangioma through minima-lly invasive techniques under the concept of rapid rehabilitation.

Efficacy and safety of minimally invasive laparoscopic surgery under general anesthesia for ovarian cancer

BACKGROUND Ovarian cancer is one of the most common malignant tumors in female reproductive system in the world,and the choice of its treatment is very important for the survival rate and prognosis of patients.Traditional open surgery is the main treatment for ovarian cancer,but it has the disadvantages of big trauma and slow recovery.With the continuous development of minimally invasive technology,minimally invasive laparoscopic surgery under general anesthesia has been gradually applied to the treatment of ovarian cancer because of its advantages of less trauma and quick recovery.However,the efficacy and safety of minimally invasive laparoscopic surgery under general anesthesia in the treatment of ovarian cancer are still controversial.AIM To explore the efficacy and safety of general anesthesia minimally invasive surgery in the treatment of ovarian cancer.METHODS The clinical data of 90 patients with early ovarian cancer in our hospital were analyzed retrospectively.According to the different surgical treatment methods,patients were divided into study group and control group(45 cases in each group).The study group received minimally invasive laparoscopic surgery under general anesthesia for ovarian cancer,while the control group received traditional open surgery for ovarian cancer.The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire(EORTC QLQ-C30),clinical efficacy and safety of the two groups were compared.RESULTS The intraoperative blood loss,length of hospital stay,postoperative gas evacuation time,and postoperative EORTC QLQ-C30 score of the study group were significantly better than those of the control group(P<0.05).The incidence of postoperative complications in the study group was significantly lower than in the control group(P<0.05).The two groups had no significant differences in the preoperative adrenocorticotropic hormone(ACTH),androstenedione(AD),cortisol(Cor),cluster of differentiation 3 positive(CD3+),and cluster of differentiation 4 positive(CD4+)indexes(P>0.05).In contrast,postoperatively,the study group's ACTH,AD,and Cor indexes were lower,and the CD3+and CD4+indexes were higher than those in the control group(P<0.05).CONCLUSION Minimally invasive laparoscopic surgery under general anesthesia in patients with early ovarian cancer can significantly improve the efficacy and safety,improve the short-term prognosis and quality of life of patients,and is worth popularizing.

Effect of ankle versus thigh tourniquets on post-operative pain in foot and ankle surgery

BACKGROUND Tourniquets are commonly used in elective extremity orthopaedic surgery to reduce blood loss,improve visualization in the surgical field,and to potentially reduce surgical time.There is a lack of consensus in existing guidelines regarding the optimal tourniquet pressure,placement site,and duration of use.There is a paucity of data on the relationship between the site of a tourniquet and postoperative pain in foot and ankle surgery.AIM To explore the relationship between tourniquet site and intensity of post-operative pain scores in patients undergoing elective foot and ankle surgery.METHODS Retrospective analysis of prospectively collected data on 201 patients who underwent foot and ankle surgery in a single institution was undertaken.Intraoperative tourniquet duration,tourniquet pressure and site,and postoperative pain scores using Visual Analogue Score were collected in immediate recovery,at six hours and at 24 h post-op.Scatter plots were used to analyse the data and to assess for the statistical correlation between tourniquet pressure,duration,site,and pain scores using Pearson correlation coefficient.RESULTS All patients who underwent foot and ankle surgery had tourniquet pressure of 250 mmHg for ankle tourniquet and 300 mmHg for thigh.There was no correlation between the site of the tourniquet and pain scores in recovery,at six hours and after 24 h.There was a weak correlation between tourniquet time and Visual Analogue Score immediately post-op(r=0.14,P=0.04)but not at six or 24 h post-operatively.CONCLUSION This study shows that there was no statistically significant correlation between tourniquet pressure,site and postop pain in patients undergoing foot and ankle surgery.The choice of using a tourniquet is based on the surgeon's preference,with the goal of minimizing the duration of its application at the operative site.

Framework for a Computer-Aided Treatment Prediction (CATP) System for Breast Cancer

This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagno-sis and frequent screening.Mammography has been the most utilized breast ima-ging technique to date.Radiologists have begun to use computer-aided detection and diagnosis(CAD)systems to improve the accuracy of breast cancer diagnosis by minimizing human errors.Despite the progress of artificial intelligence(AI)in the medical field,this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used.Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money.To develop the prediction model,data from 336,525 patients from the SEER dataset were split into training(80%),and testing(20%)sets.Decision Trees,Random Forest,XGBoost,and CatBoost are utilized with feature importance to build the treatment prediction model.The best overall Area Under the Curve(AUC)achieved was 0.91 using Random Forest on the SEER dataset.

Cardiac rehabilitation after cardiac surgery:An important underutilized treatment strategy

Physical inactivity remains in high levels after cardiac surgery,reaching up to 50%.Patients present a significant loss of functional capacity,with prominent muscle weakness after cardiac surgery due to anesthesia,surgical incision,duration of cardiopulmonary bypass,and mechanical ventilation that affects their quality of life.These complications,along with pulmonary complications after surgery,lead to extended intensive care unit(ICU)and hospital length of stay and significant mortality rates.Despite the well-known beneficial effects of cardiac rehabilitation,this treatment strategy still remains broadly underutilized in patients after cardiac surgery.Prehabilitation and ICU early mobilization have been both showed to be valid methods to improve exercise tolerance and muscle strength.Early mobilization should be adjusted to each patient’s functional capacity with progressive exercise training,from passive mobilization to more active range of motion and resistance exercises.Cardiopulmonary exercise testing remains the gold standard for exercise capacity assessment and optimal prescription of aerobic exercise intensity.During the last decade,recent advances in healthcare technology have changed cardiac rehabilitation perspectives,leading to the future of cardiac rehabilitation.By incorporating artificial intelligence,simulation,telemedicine and virtual cardiac rehabilitation,cardiac surgery patients may improve adherence and compliance,targeting to reduced hospital readmissions and decreased healthcare costs.

Influence of humanistic care-based operating room nursing on safety,recovery,and satisfaction after radical surgery for colorectal carcinoma

BACKGROUND Radical surgery is a preferred treatment for colorectal carcinoma,wherein nursing intervention is essential for postoperative recovery and prevention of complications.Recently,the application of humanistic care in medical care has attracted attention.Humanistic care emphasizes comprehensive care,with importance attached to patients’physical needs as well as psychological and emotional support to provide more humane and personalized care services.However,no clinical reports have examined the use of humanistic care in patients undergoing radical surgery for colorectal carcinoma.AIM To investigate the influence of humanistic care-based operating room nursing on the safety,postoperative recovery,and nursing satisfaction of patients who have undergone radical surgery for colorectal carcinoma.METHODS In total,120 patients with rectal cancer who underwent surgery in Zhongnan Hospital of Wuhan University between August 2023 and March 2024 were selected and grouped based on the nursing methods employed.Of these patients,55 were treated with routine nursing intervention(control group)and 65 were provided humanistic care-based operating room nursing(research group).The patients’vital signs were recorded,including systolic/diastolic blood pressure(SBP/DBP)and heart beats per minute(BPM),as well as serum stress indices,including norepinephrine(NE),adrenal hormone(AD),and cortisol(Cor).Postoperative recovery and complications were also recorded.Patients’negative emotions,life hope,and nursing satisfaction were evaluated using the Self-rating Depression/Anxiety Scale(SDS/SAS),Herth Hope Index(HHI),and self-deve-loped nursing satisfaction questionnaire,respectively.RESULTS During emergence from anesthesia,SBP,DBP,and BPM levels were found to be lower in the research group than those in the control group,also serum Cor,AD,and NE levels were lower.In addition,the research group had shorter operative,awakening,anal exhaust,first postoperative ambulation,drainage tube removal,intestinal recovery,and hospital times.The total complication rate and the SDS and SAS scores were lower in the research group than those in the control group.The HHI and nursing satisfaction scores were higher in the research group.CONCLUSION Humanistic care-based operating room nursing can mitigate physiological stress responses,reduce postoperative complications,promote postoperative recovery,relieve adverse psychological emotions,and enhance life hope and nursing satisfaction in patients undergoing radical surgery for colorectal carcinoma,which can be popularized in clinical practice.