Objective To construct YOLOv10 based artificial intelligence(AI)models for the automatic detection in small bowel capsule endoscopy(SBCE)images.Methods SBCE data from two centers was collected,including 23 115 images and 35 412 annotated labels covering 11 categories of small bowel lesions.The images were annotated using the LabelMe tool and converted into the YOLO format required for deep learning model development.The pre-trained YOLOv10 and YOLOv8 models were used for transfer learning training on the constructed dataset.Model performance was comprehensively evaluated using metrics such as precision,accuracy,sensitivity,specificity,false-positive rate,and detection speed.Finally,the models were deployed on local computers for real-time detection of SBCE images and videos.Results Six different versions of YOLO object detection models were developed,namely YOLOv8n,YOLOv8s,YOLOv8m,YOLOv10n,YOLOv10s,and YOLOv10m.On the validation set,YOLOv10s model achieved the best mAP50(0.795);although its inference latency was not the fastest(4.803 ms/img),it met the requirements for clinical application.On the test set,YOLOv10s performed well,with an accuracy of 92.69%,a sensitivity of 89.23%,and a false-positive rate of 4.78%.Especially,in category-specific inference,the highest sensitivity was for"bleeding"at 96.41%,while the lowest was for"narrowing"at 82.29%.Conclusion The model constructed based on YOLOv10 neural network can rapidly and accurately detect and classify various small bowel lesions,exhibiting significant clinical application potential.

Objective To test the hypothesis that backward crawling and forward crawling share similar inter-joint coordination patterns,thus providing potential evidence for the application of backward crawling in rehabilitation training.Methods The acceleration signals in the X,Y,and Z directions for 9 joints(including bilateral wrists,elbows,shoulders,knees,and hips)in 9 volunteers during forward and backward crawling were collected using a custom signal acquisition system,and the pressure signals were also recorded when the palms contacted the ground.The collected acceleration signals were preprocessed,segmented into cycles,and vectorized.Based on the pressure signals,a single crawling cycle was divided into support phase and swing phase.In addition,principal component analysis was applied to extract inter-joint coordination in limbs at various scales(sagittal,coronal,and transverse planes).Pearson correlation coefficients of inter-joint coordination patterns were compared between forward and backward crawling in support period,swing period,and full cycle.Results The correlation coefficients for coordination patterns in the full cycle at the transverse plane scale were 0.813 5(PC1)and 0.837 5(PC2),and the correlation coefficient of the support period PC2 was 0.901 8.At the sagittal plane scale,the correlation coefficient of the support period PC1 was 0.948 5.Conclusion The study provides preliminary evidence that limb motion coordination patterns during backward crawling are similar to those observed during forward crawling.Future research will further explore the effects of backward crawling on functional rehabilitation in individuals with motor impairments.

The study introduces the technical characteristics(product type,usage mode,temperature control mode,use environment,etc.)of temperature control equipment for extracorporeal membrane oxygenation,and evaluates effectiveness and safety of the product based on its technical characteristics.Main contributions of the study are as follows:(1)clarifying key technical indicators for effectiveness and safety,as well as common risk sources;(2)putting forward the requirements for the researches on product performance,cleaning and disinfection,and stability.

As a non-invasive physiological indicator,electroencephalography signal provides an objective assessment of psychological stress levels among nursing staff in major public health emergencies,offering a scientific basis for targeted psychological interventions while overcoming the limitations of subjective bias inherent in traditional questionnaire-based methods.A psychological stress classification model based on bidirectional long short-term memory and attention mechanism is proposed to classify the psychological stress of clinical nurses more effectively by analyzing their electroencephalography signals.Experimental results show that the proposed model exhibits better classification performance than the traditional long short-term memory model on the DREAMER dataset,the Feeling Emotions dataset and the self-built dataset.This study provides a novel approach for assessing psychological stress,which is helpful to improve the pertinence and effectiveness of clinical nursing work.

Due to the constraints of the surgical environment and operational space,laparoscopic surgical instruments employ wire-driven mechanisms.However,factors such as wire rigidity,hysteresis,and motor drive limitations result in the end-effector accuracy of surgical instruments failing to meet ideal requirements.To address the shortcomings of existing multi-degree-of-freedom laparoscopic surgical instruments in achieving end-effector precision,a motion compensation algorithm based on the Autogluon algorithm for a 4-degree-freedom laparoscopic surgical instrument is proposed.A single-degree-of-freedom surgical instrument driven by wire ropes was constructed,and machine learning was utilized to estimate the end-effector position.This estimated position served as a feedback compensation condition to control the end-effector of the surgical instrument.To validate the correctness of this method,it was compared with approaches such as neural networks,linear regression,decision trees,Gaussian processes,and support vector machines.The results demonstrated that the proposed method achieved the smallest mean squared error,maximum error,and mean absolute error,thereby verifying its effectiveness.

Objective To improve the three-dimensional gait and walking function of hemiplegic patients and enhance their quality of life through training with an end-driven lower limb rehabilitation robot.Methods Seventy elderly stroke patients with hemiplegia were enrolled and randomly divided into control group(n=35)and observation group(n=35).The patients in control group received conventional rehabilitation therapy after their conditions were stable,while the patients in observation group were trained by therapists with an end-driven lower limb rehabilitation robot besides the same treatment as control group.The rehabilitation efficacy was analyzed in terms of National Institute of Health Stroke Scale(NIHSS)score,Barthel Index(BI)score,Berg Balance Scale(BBS)score,Fugl-Meyer Assessment:Lower Extremity(FMA-LE),three-dimensional gait and satisfaction with nursing care.Results Before intervention,there was no significant difference between two groups in NIHSS score,BI score,BBS score,FMA-LE score,gait spatiotemporal parameters,gait temporal phase parameters,and ranges of motion of lower limb joints(P>0.05).After intervention,NIHSS score decreased,while BI score,BBS score and FMA-LE score increased in both groups(P<0.05),and the improvements were more obvious in observation group than in control group(P<0.05).The interventions in both groups increased step speed,step frequency and stride length,while reducing step width(P<0.05),with more significant improvements observed in observation group than in control group(P<0.05).Compared with those before intervention,the percentage of support period of the affected lower limb increased,and the ratio of the support period of the healthy side to that of the affected side and the percentage of double support period decreased(P<0.05),and these indexes were improved more prominent in observation group(P<0.05).The ranges of motion of lower limb joints in both groups were significantly greater than those before intervention(P<0.05),with greater ranges of motion in observation group than in control group(P<0.05).The total satisfaction with nursing care in observation group was higher than that in control group(97.14%vs 82.86%,P<0.05).Conclusion End-driven lower limb rehabilitation robot-assisted training can further improve the neurological function,lower limb function and balance ability in hemiplegic patients,with high patient satisfaction.The rehabilitation efficacy can be objectively assessed through three-dimensional gait analysis.

Objective To explore the clinical efficacy of swallowing training combined with Vitalstim neuromuscular electrical stimulation in patients with dysphagia after stroke for providing a scientific basis for improving the swallowing function.Methods A total of 100 patients with post-stroke dysphagia were enrolled and randomly divided into control group(n=50,conventional swallowing training)and observation group(n=50,conventional swallowing training+Vitalstim neuromuscular electrical stimulation),with a total treatment course of 3 weeks.The swallowing functions before and after treatment was compared between two groups.Results After treatment,observation group scored lower on the water swallow test and the Rosenbek Penetration-Aspiration Scale(P<0.05),while higher on the Functional Oral Intake Scale and the Functional Independence Measure as compared with control group(P<0.05).In addition,the score of Nutritional Risk Screening 2002(NRS2002)was higher in observation group than in control group(P<0.05).Conclusion The combination of swallowing training and Vitalstim neuromuscular electrical stimulator can effectively improve the swallowing function of stroke patients and has high clinical application value,worthy of further promotion and research.

Magnetic induction hyperthermia is a non-invasive therapeutic method that utilizes an alternating magnetic field to excite magnetic nanoparticles to generate heat,thereby destroying tumor cells.In recent years,the biosafety of metal nanoparticles,the potential impact of alternating magnetic fields,and the safety issues of magnetic induction hyperthermia in the treatment of tumors have become research hot topics.The review introduces the metabolism of metal nanoparticles in the body and their potential toxicity,discusses the effects of alternating magnetic fields on normal cells and tissues,and summarizes the researches on the safety and efficacy of magnetic induction hyperthermia in clinical applications.Through in-depth analysis of these key issues,the aim is to enhance the understanding of the safety of magnetic induction hyperthermia among clinical physicians and researchers,and promote its rational application in tumor treatment,thus providing patients with safer and more effective treatment schemes.

Objective To identify key acoustic features associated with the progression of Alzheimer's disease(AD)through voice analysis combined with machine learning and feature selection techniques,thereby constructing classification models that serve as candidate tools for the early screening of AD.Methods Voice samples from AD,mild cognitive impairment(MCI)and healthy(HC)elderly individuals were obtained from the NCMMSC2021 AD voice dataset.The voice samples underwent data preprocessing,followed by feature extraction from the eGeMAPS feature set via the OpenSmile toolkit.Classification models were obtained utilizing random forest and support vector machine(SVM)algorithms.Significance testing and feature importance ranking were conducted using Python,and the further selection of the optimal features was performed through sequential forward selection(SFS).The classification performance before and after feature selection was compared and evaluated using accuracy and the area under the receiver operating characteristic curve(AUC).Results The significant acoustic features in the classification models primarily derived from spectral slope,formant,fundamental frequency,and loudness.The optimal classification performance was achieved with the SVM model following SFS feature selection,with recognition accuracies of 0.926(AUC=0.974)for AD/MCI group,0.875(AUC=0.956)for AD/HC group,and 0.879(AUC=0.904)for MCI/HC group.Conclusion SVM model performs better than random forest model,and the use of SFS for feature selection can effectively enhance model performance.Voice analysis has the potential to serve as a valuable supplementary tool for the rapid AD assessment and screening.

Transmission X-ray tubes are relatively new devices characterized by portability,suitability for miniaturization,and low requirements for shielding,making them ideal radiation sources for kilovoltage X-ray therapy.However,their application in radiotherapy remains underexplored.An electron target model of a transmission X-ray tube is developed using the Monte Carlo toolkit TOPAS 3.8.1.The study investigates the effect of tungsten target thickness on X-ray output efficiency,finding that a tube voltage of 50 kV and a tungsten thickness of 1.4 μm yields the highest emission efficiency.Based on the energy spectrum at this optimal efficiency,polynomial fitting approach is applied to determine the corresponding aluminum filter thickness for mean energies ranging from 20 keV to 35 keV,achieving a mean fitting error of 0.91%.Next,the study simulates dose deposition in a water phantom for spectra with different mean energies and various source-to-surface distances,and plots percent-depth-dose curves,relative normalized dose-depth curves,and relative normalized dose histograms under each treatment condition.Finally,the simulated results are compared with experimental data from the intraoperative radiotherapy system Intrabeam and the superficial X-ray therapy unit SRT-100,obtaining average relative errors of 3.71%and 4.38%,respectively.These findings provide a theoretical foundation for further optimization of transmission X-ray tubes in kilovoltage radiotherapy.