Objective To explore the research progress of the multimodal clinical support system (CSS). Methods With recognized development and operation of the multi-model CSS, and compared to the traditional CSS, to explore the research progress of the multimodal CSS. Results Based on the realization of the concept, purpose and characteristics of the multimodal CSS, it has been known that the international research progress of the multimodal CSS. Conclusion The developing and evolving of the CSS model have offered a new assist to the multi-disciplinary treatment model, and have enhanced the improving system associated with the practice of evidence-based medicine. However, the application of clinical support system program (CSSP) in our country still needs more research.
Objective To improve hand hygiene executive ability of healthcare workers in medical institutions in Anhui Province by multi-modal interventions with the administrative intervention as the guide. Methods The PDCA management mode was adopted in a step-by-step implementation of plan, implementation, inspection, improvement, and effectiveness evaluation in Anhui Province from April 2014 to December 2016. The management indicators of hand hygiene before and after the intervention in 1 353 hospitals were investigated and evaluated. Results The overall evaluation of the hand hygiene at the end of the implemention showed that 85.29% (58/68) of the tertiary hospitals, 84.07% (227/270) of the second-class hospitals and 66.63% (595/893) of the primary-level hospitals had well-equipped hand hygiene facilities. About 92.65% (63/68) of the tertiary hospitals, 100.00% (270/270) of the second-class hospitals and 50.06% (447/893) of the primary-level hospitals had staff training of hand hygiene knowledge. The compliance of hand hygiene before and after intervention increased from 36.68% to 61.93%, the correct rate of hand washing increased from 37.60% to 89.28%, the awareness rate of related knowledge increased from 41.20% to 86.07%, and the dosage of hand disinfectant increased from 2.59 mL to 7.10 mL. Conclusion To take multi-model interventions with the administrative intervention as the guide, can effectively improve the quality of hand hygiene management and the executive force.
Objective To understand the effect of World Health Organization(WHO) multimodal hand hygiene improvement strategy on hand hygiene compliance among acupuncturists. Methods All the acupuncturists in departments (Department of Acupuncture, Department of Encephalopathy, Department of Orthopedics and Traumatology) with acupuncture programs in Xi’an Hospital of TCM were chosen in this study between September 2015 and August 2016. Based on the WHO multimodal hand hygiene improvement strategy, comprehensive measures were regulated among acupuncturists. Hand hygiene compliance and accuracy, and hand hygiene knowledge score were compared before and after the strategy intervention. Then, the effects of key strategies were evaluated. Results Overall hand hygiene compliance rate, accuracy and knowledge scores increased from 51.07%, 19.86% and 81.90±2.86 before intervention to 72.34%, 51.70%, and 98.62±2.92 after intervention (P<0.05). Hand hygiene compliance rates also increased in various occasions such as before contacting the patient, after contacting the patient, before acupuncture treatment, and before acupuncture needle manipulation (P<0.05). Conclusion Hand hygiene compliance in acupuncturists can be significantly improved by the implementation of WHO multimodal hand hygiene improvement strategy.
ObjectiveTo observe multimodal imaging characteristics in eyes with focal choroidal excavation (FCE) and preliminarily analyze the risk factors in FCE with complications correlated with RPE.MethodsA retrospective case series. Thirty-one patients (31 eyes) with monocular FCE, first identified by spectral-domain (SD)-OCT in the Eye Center of The Second People’s Hospital of Foshan from December 2014 to December 2018, were involved in this study. There were 14 males and 17 females, with the mean age of 45.84±13.57 years. All patients underwent BCVA, optometry, and SD-OCT examinations. FFA and ICGA were simultaneously performed in 3 FCE patients with RPE complications. The subfoveal choroidal thickness (SFCT) and excavation width were measured with enhanced depth imaging OCT (EDI-OCT). The eyes with FCE were divided into two groups (FCE alone group 17 eyes vs. FCE complication group 14 eyes), based on whether complicated by RPE dysfunction. Among 14 eyes of FCE complication group, 7 (22.6%) with choroidal neovascularization, 4 (12.9%) with central serous chorioretinopathy, 1 (3.2%) with polypoidal choroidal vasculopathy, and 2 (6.5%) with RPE detachment. No significant difference was found in the mean age (t=0.87), gender composition (χ2=0.06), ocular laterality (χ2=2.58), and spherical equivalent (t=−0.81) between two groups, respectively (P＞0.05), except that the BCVA was significantly different (t=−2.11, P＜0.05). The SFCT and excavation width of eyes in both groups and the ICGA imaging characteristics of eyes in FCE complication group were analyzed. Risk factors of FCE with RPE complications were analyzed by logistic regression analysis.ResultsThirty-three excavations were identified in 31 eyes with FCE. The mean SFCT was 167.00±85.18 μm in FCE alone group vs. 228.36±67.95 μm in FCE complication group, while the excavation width was 645.00±231.93 μm vs. 901.00±420.55 μm and they were both significantly different (P＜0.05). Logistic regression analysis showed the SFCT (OR=1.016, P=0.026) and excavation width (OR=1.004, P=0.034) were risk factors for RPE complications of FCE. EDI-OCT showed the RPE at the excavation was impaired or vulnerable in all eyes of the FCE alone group, especially at the boundary area of excavation. The RPE damages were located at the boundary area of excavation in 10 eyes (71.4%) of FCE complication group. Constant choroidal hypofluorescence and filling defect were observed under the excavation in 3 eyes with ICGA imaging.ConclusionsSFCT and excavation width may be risk factors for RPE complications of FCE. Impairment of RPE at boundary area of excavation and focal choroidal ischemia or aberrant circulation under the excavation may correlate with the development of FCE complications.
Recently, deep learning has achieved impressive results in medical image tasks. However, this method usually requires large-scale annotated data, and medical images are expensive to annotate, so it is a challenge to learn efficiently from the limited annotated data. Currently, the two commonly used methods are transfer learning and self-supervised learning. However, these two methods have been little studied in multimodal medical images, so this study proposes a contrastive learning method for multimodal medical images. The method takes images of different modalities of the same patient as positive samples, which effectively increases the number of positive samples in the training process and helps the model to fully learn the similarities and differences of lesions on images of different modalities, thus improving the model's understanding of medical images and diagnostic accuracy. The commonly used data augmentation methods are not suitable for multimodal images, so this paper proposes a domain adaptive denormalization method to transform the source domain images with the help of statistical information of the target domain. In this study, the method is validated with two different multimodal medical image classification tasks: in the microvascular infiltration recognition task, the method achieves an accuracy of (74.79 ± 0.74)% and an F1 score of (78.37 ± 1.94)%, which are improved as compared with other conventional learning methods; for the brain tumor pathology grading task, the method also achieves significant improvements. The results show that the method achieves good results on multimodal medical images and can provide a reference solution for pre-training multimodal medical images.
ObjectiveTo observe the clinical and imaging characteristics of acute idiopathic macular degeneration (AIM).MethodsA retrospective clinical study. From March 2016 to January 2018, 5 eyes (5 AIM patients) in The Second People's Hospital of Yunnan Province were included in the study. Among them, there were 4 males (4 eyes) and 1 female (1 eye); all patients were monocular with the average age of 34.2 years. The course of illness from onset of symptoms to treatment was 4-22 days. All affected eyes were examined by BCVA, fundus color photography, OCT, FAF, and FFA. Among 5 eyes, 1 eye with optic disc vasculitis was given oral glucocorticoid treatment; 4 eyes were not interfered after the diagnosis. ResultsThe follow-up time was 6 months. During follow-up, BCVA, fundus color photography, and OCT examination were performed. The results were all a sudden decrease in monocular vision, accompanied by visual distortion or central dark spots. At the first visit, the BCVA was 0.1, 0.2, 0.2, 0.05, and 0.5; at the last follow-up, the BCVA of the affected eye was 0.8, 0.6, 0.5, 0.5, and 1.0, respectively. Fundus color photography showed that at the first diagnosis, all the affected eyes showed irregular round yellow-white lesions in the macular area, including 1 eye with small patches of hemorrhage and 1 eye with pseudopyous changes in the macular area. Two to three weeks after the initial diagnosis, the yellowish-white lesions and bleeding in the macular area were basically absorbed. The center of the lesion showed weak pseudopod-like fluorescence, and the surrounding area was surrounded by strong fluorescence in FAF examination. The irregular and strong fluorescence in the early macular area and accumulation of late fluorescein in FFA examination. One eye was receivied glucocorticoid therapy. The upper layer of the retinal nerve in the macular area was detached, and the inferior space showed focal strong reflective material in 3 eyes in OCT examination. At the first diagnosis, the retinal neuroepithelial layer was detached, the top of the RPE layer was irregular with strong reflective material, and the structure of the ellipsoid zone and the chimera zone was unclear; as the course of the disease prolonged, the outer retinal structure recovered.ConclusionsAIM is characterized by inflammatory exudative changes in the outer layer of the retina in the macular area; FFA is characterized by strong subretinal disc-like fluorescence or multifocal weak fluorescence in the macular area; OCT mainly manifests as neuroepithelial detachment and changes in the outer retina and RPE, The structure can be restored by itself.
Objective To summarize recent advances on preoperative staging strategies in rectal cancer. Methods Relevant references about preoperative staging strategies were collected and reviewed. The multimodal preoperative evaluation (MPE) system recently documented was focused on. Results The comparably accurate T and M stage could be achieved preoperatively by following an appropriate available method; however, the N stage’s accuracy was still not satisfying. The MPE system， incorporating with the advantages of transrectal ultrasound， computerized tomography and serum amyloid A protein in a multi-disciplinary mode could display the most accurate preoperative staging for rectal cancer currently. Conclusion The MPE has potential prospects in preoperative staging of rectal cancer， and can provide the most accurate preoperative staging for rectal cancer at present.
Objective To review the research advancement of multimodal therapy for advanced gast ric carcinoma. Methods The literatures on multimodal therapy for advanced gastric carcinoma in recent years were collected and reviewed. Results The multimodal therapy , such as preoperative chemotherapy , preoperative adjuvant chemoradiotherapy , preoperative interventional chemoradiotherapy for advanced gast ric carcinoma was effective because it could increase the rate of R0 resection for the patients with advanced gastric carcinoma. And it can decrease the mortality rate after operation , extend the overall survival time and improve patients’life quality. Conclusion Multimodal therapy is a promising method for the treatment of advanced gastric carcinoma and it should be further developed.
Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer’s disease.
In recent years, the incidence of thyroid diseases has increased significantly and ultrasound examination is the first choice for the diagnosis of thyroid diseases. At the same time, the level of medical image analysis based on deep learning has been rapidly improved. Ultrasonic image analysis has made a series of milestone breakthroughs, and deep learning algorithms have shown strong performance in the field of medical image segmentation and classification. This article first elaborates on the application of deep learning algorithms in thyroid ultrasound image segmentation, feature extraction, and classification differentiation. Secondly, it summarizes the algorithms for deep learning processing multimodal ultrasound images. Finally, it points out the problems in thyroid ultrasound image diagnosis at the current stage and looks forward to future development directions. This study can promote the application of deep learning in clinical ultrasound image diagnosis of thyroid, and provide reference for doctors to diagnose thyroid disease.