As a fundamental aspect of bone fracture treatment, fracture reduction plays a decisive role in restoring the structural integrity and function of bones. At present, fracture reduction techniques mostly rely on semi-automatic interaction methods or healthy-side bone templates for registration, which have many limitations in clinical practice. In order to enhance treatment efficiency and accuracy, an automatic fracture reduction algorithm is proposed. This algorithm utilizes the similarity of fracture cross-sections for registration, thereby reducing the workload of physicians and eliminating the need for a healthy-side bone template. Initially, the closed edge is identified and extracted by analyzing the differences in the fracture surface and the calorific value diagram of the roughness distribution. Next, the fracture section is determined by using the identified closed edge as a guideline for regional expansion and similarity matching. During the registration phase, the iterative closest point (ICP) algorithm is highly sensitive to distance. Therefore, the geometric features of point clouds are incorporated into the objective function of the registration algorithm to mitigate the influence of noise, and fracture section registration is implemented one by one. Finally, the algorithm is tested and compared on 180 simulated datasets and 16 publicly available datasets. The results show that the proposed algorithm significantly improves the registration accuracy, and the registration error of clinical bone fracture cases is controlled within 1.7 mm.
Algorithms ; Fractures, Bone/therapy* ; Humans

The gradient coils of MRI equipment can induce vibrations in implantable medical devices, causing periodic vibrations of implantable medical devices with respect to the surrounding tissue. This not only results in instrument failure but also causes discomfort to the patient. Therefore, studying the vibration characteristics of implantable devices under different scanning sequences and the orientation of the device relative to the magnetic field is crucial for comprehending vibration performance. This study observed the vibration spectra of a full cranial bone-implanted neurostimulator by using laser vibrometry under typical rapid imaging sequences and explored the impact of different magnetic field orientations on vibration. The results demonstrated that the rapid echo sequences induced diverse and rich vibration components, whereas the planar echo sequences caused relatively simple vibrations. Additionally, the strongest vibrations normally occurred in the maximum conductive surface parallel to the phase-coded direction. It revealed the factors influencing the vibrations of skull fixation active implantable devices and provided guidance for enhancing device safety and protecting patient well-being during MR examinations.
Vibration ; Magnetic Resonance Imaging ; Deep Brain Stimulation/instrumentation* ; Magnetic Fields ; Humans

With the development of artificial intelligence technology and the growing demand for minimally invasive surgery, the intelligentization of minimally invasive surgery has become a current research hotspot. Surgical instrument segmentation is a highly promising technology that can enhance the performance of minimally invasive endoscopic imaging systems, surgical video analysis systems, and other related systems. This article summarizes the semantic and instance segmentation methods of minimally invasive surgical instruments based on deep learning, deeply analyzes the supervision methods of training algorithms, network structure improvements, and attention mechanisms, and then discusses the methods based on the Segment Anything Model. Given that deep learning methods have extremely high requirements for data, current data augmentation methods have also been explored. Finally, a summary and outlook on instrument segmentation technology are provided.
Artificial Intelligence ; Minimally Invasive Surgical Procedures/instrumentation* ; Algorithms ; Deep Learning ; Humans ; Image Processing, Computer-Assisted

In recent years, cardiovascular disease has become a common disease. With the development of machine learning and big data technologies, the processing ability of electrocardiogram (ECG) signals has been greatly enhanced through new computer technologies, enabling the auxiliary diagnosis technology for cardiovascular disease (CVD) to achieve new improvements. This article discusses the application of machine learning in ECG processing, especially in the auxiliary diagnosis of diseases. Firstly, the conventional signal preprocessing methods are introduced, and then the EEG signal processing methods based on feature extraction and fuzzy classification are explored. Secondly, the application of auxiliary diagnosis in CVD is further summarized. Finally, the advantages and disadvantages of the two methods are analyzed, and based on this, a design of an auxiliary diagnostic system compatible with the two methods is proposed, providing a new perspective for similar applied researches in the future.
Machine Learning ; Cardiovascular Diseases/diagnosis* ; Humans ; Electrocardiography ; Signal Processing, Computer-Assisted ; Diagnosis, Computer-Assisted ; Fuzzy Logic ; Electroencephalography

Electrical impedance tomography (EIT) is a technique that uses an array of electrodes to deliver safe stimulating currents and measures the boundary voltages between adjacent electrode pairs in the array in sequence. Subsequently, it reconstructs the impedance distribution in all or part of the tissue using reconstruction algorithms to achieve structural and functional imaging. Lung EIT technology features continuity, being radiation-free and non-invasive, and it can be used for real-time dynamic monitoring of the lungs in critically ill patients. This paper introduces the basic principles of lung EIT, analyzes the research progress and existing problems of the technology from the perspectives of hardware systems, imaging algorithms, and clinical applications (such as lung ventilation, lung perfusion, and lung function assessment), and discusses the development direction to provide ideas for expanding the clinical application of lung EIT.
Electric Impedance ; Humans ; Tomography/methods* ; Lung Diseases/therapy* ; Algorithms

OBJECTIVE: To lay a foundation for formulating clinical evaluation and regulatory policies regarding radiotherapy equipment in China. METHODS: Comprehensive retrieval and comparison of the regulatory requirements for radiotherapy equipment from regulatory agencies in the United States, the European Union, and China are conducted, and their similarities and differences are analyzed. RESULTS: For mature radiotherapy systems in the European and American regions, the comparison of performance parameters serves as an important basis for determining whether a product can be marketed. Both the European Union and China regard the clinical evaluation report as a crucial part of the medical device review and submission process. CONCLUSION Identifying clear state of the art standards, performance parameters, and clinical indicator parameters, and establishing relevant technical guidelines are important directions for promoting the standardized development of radiotherapy equipment supervision.
Radiotherapy/standards* ; United States ; China ; European Union

At present, the C-arm structure accelerators commonly used in radiotherapy equipment are complex in operation and have potential safety hazards when realizing non-coplanar treatment. By combining with medical robotic arm technology, a spherical radiotherapy accelerator motion system is designed. The beam module is clamped by the medical robotic arm structure to achieve three-dimensional multi-angle irradiation treatment within the non-coplanar angle range. Firstly, the rotating mechanism, beam module, and MLC module of the spherical radiotherapy equipment are designed. Then, the double-plane counterweight method is used to calculate the dynamic balance of the equipment, ensuring that the beam center point does not rotate during the treatment process. Finally, the strength check and reliability analysis of the transmission component gear are conducted. The results show that the designed spherical radiotherapy accelerator motion system can meet the requirements of stable, accurate, and fast precision radiotherapy, which is conducive to improving the treatment efficiency.
Particle Accelerators/instrumentation* ; Equipment Design ; Reproducibility of Results ; Radiotherapy/instrumentation*

OBJECTIVE: Automatic planning is a commonly used alternative to manual planning. This study evaluated the clinical performance of automatic plans available in commercial treatment planning systems for nasopharyngeal carcinoma (NPC) treatment by comparing automatic planning with manual planning. METHODS: A total of 14 patients with nasopharyngeal carcinoma were enrolled in the study. For each patient, three different sets of clinical goals were used to generate three hybrid automatic plans based on 3D dose distribution prediction and three automatic plans based on script, respectively, which were compared with the manual plans used in clinic. RESULTS: The dose coverage performance of the automatic planning based on 3D dose distribution prediction on the planning target volume (PTV) was comparable to that of the manual planning. Automatic planning based on 3D dose prediction achieved the level of manual planning in most organs at risk. However, automatic planning based on scripts did not perform well in the prediction of some organs at risk, especially the parotid gland. CONCLUSION The hybrid automatic plan based on 3D dose distribution prediction can reach the level of manual planning and have good robustness with the change of clinical objective.
Humans ; Nasopharyngeal Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods* ; Nasopharyngeal Carcinoma ; Male ; Female ; Middle Aged ; Adult ; Carcinoma ; Radiotherapy Dosage

OBJECTIVE: To investigate the clinical value of MRI modified water-lipid separation technique (mDIXON-Quant) in terms of T2* values and fat fraction (FF) values for evaluating the degree of cervical intervertebral disc degeneration. METHODS: A total of 118 patients who underwent routine MRI of the cervical vertebra and the mDIXON-Quant examination in Fuyang People's Hospital from March 2019 to January 2024 were collected. The T2* values of C2/3 to C6/7 intervertebral discs and FF values of the upper and lower vertebral bodies were measured. Cervical intervertebral disc degeneration was graded according to the Pfirrmann criteria. The T2* values and FF values of different patients were compared with the Pfirrmann grades, and the correlation between T2* values, FF values, and Pfirrmann grades was analyzed by Spearman correlation. RESULTS: The T2* values of cervical intervertebral discs in grades I, II, III, IV, and V all showed a decreasing trend ( P＜0.05). The decreasing order of FF values in the upper cervical vertebra was IV, V, III, II, and I ( F=93.28, P＜0.05), and the decreasing order of FF values in the lower cervical vertebra was IV, III, V, II, and I ( F=112.037, P＜0.05). Spearman correlation analysis showed that the T2* values of cervical intervertebral discs were negatively correlated with the Pfirrmann grades ( P＜0.05), and the FF values of the upper and lower vertebrae were positively correlated with the Pfirrmann grades ( P＜0.05). CONCLUSION The mDIXON-Quant technique can be used to quantify the T2* values and FF values of cervical intervertebral disc degeneration and plays an important role in accurate clinical diagnosis and evaluation of treatment effects.
Humans ; Intervertebral Disc Degeneration/diagnostic imaging* ; Cervical Vertebrae/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Adult ; Intervertebral Disc/diagnostic imaging* ; Lipids

OBJECTIVE: Referring to the application of the NEH (Nawaz-Enscore-Ham) algorithm in flow shop scheduling, this paper proposes an optimization method of hospital medical consumables discrete scheduling based on the extended NEH algorithm, in order to obtain the optimal medical consumables discrete scheduling scheme and achieve the optimization of medical consumables discrete scheduling. METHODS: Thoroughly analyze the scheduling needs of medical consumables in hospitals, predict the demand for medical consumables in each department. Construct a discrete scheduling optimization model for medical consumables and determine the constraint conditions for building the model (residual coefficient matrix, efficiency constraint matrix, and time window function). Then, solve the discrete scheduling optimization model for medical consumables based on the extended NEH algorithm. RESULTS: By comparison the data before and after the experiment, it is found that the discrete scheduling time and cost of medical consumables in experimental departments have decreased to varying degrees, and all the differences are statistically significant ( P<0.05). CONCLUSION Through experiments, it is known that the optimized medical consumables discrete scheduling scheme proposed meets the requirements of discrete scheduling time and cost, providing a better solution for hospital medical consumables discrete scheduling.
Algorithms ; Materials Management, Hospital ; Appointments and Schedules