Objective To evaluate the cl inical effects of anterior segmental decompression and autograft fusion in treating multi-level cervical spondylotic myelopathy (CSM). Methods Between January 2007 and May 2009, 23 patients with multi-level CSM were treated with anterior segmental decompression, autograft fusion, and internal fixation. There were 16 males and 7 females with an average age of 58 years (range, 49-70 years). Consecutive 3 segments of C3,4, C4, 5, and C5, 6 involvedin 15 cases and C4, 5, C5, 6, and C6, 7 in 8 cases. All patients suffered sensory dysfunction in l imbs and trunk, hyperactivity of tendon reflexes of both lower extremities, walking with l imp, and weakening of hand grip. Cervical MRI showed degeneration and protrusion of intervertebral disc and compression of cervical cord. The disease duration was 6 to 28 months (12.5 months on average). Japanese Orthopaedic Association (JOA) score system was adopted for therapeutic efficacy evaluation. JOA scores were recorded preoperatively, 1 week, 3 months, and 12 months postoperatively. Results Dura tear occurred in 1 case and was treated by fill ing with gelatinsponge during operation; no cerebrospinal fluid leakage was observed after operation. All the incisions healed by first intention. All cases were followed up 12 to 24 months (15.1 months on average), and no vertebral artery injury or recurrent laryngeal nerve injury occurred. The nervous symptoms in all cases were improved significantly within 1 week after operation. Lower l imb muscle strength increased, upper l imb abnormal sensation disappeared, and l imb moved more agile. A 2-mm collapses of titanium mesh into upper terminal plate were found in 1 case and did not aggravated during followup.The other internal fixator was in appropriate situation, and the fusion rate was 100%. The JOA score increased from 9.1 ±0.3 preoperatively to 14.3 ± 0.4 at 12 months postoperatively with an improvement rate of 65.8% ± 0.2%, showing significant difference (P lt; 0.01). According to Odom evaluation scale, the results were excellent in 10 cases, good in 8 cases, fair in 4 cases, and poor in 1 case. Conclusion Anterior segmental decompression and autograft fusion is a recommendable technique for multi-level CSM, which can make full decompression, conserve the stabil ity of cervical cord, and has high fusion rate.
Through collecting and synthesizing the paper concerning the method of dealing with heterogeneity in the meta analysis, to introduce the multi-levels statistical models, such as meta regression and baseline risk effect model based on random effects, and random effects model based on hierarchical bayes, and to introduce their application of controlling the meta analysis heterogeneity. The multi-levels statistical model will decompose the single random error in the traditional model to data structure hierarchical. Its fitting effect can not only make the meta-analysis result more robust and reasonable, but also guide clinical issues through the interpretation of association variable.
ObjectiveTo assesse the effectiveness of anterior cervical discectomy and fusion with Cage alone in treating multi-level cervical degenerative disease. MethodsBetween August 2010 and August 2012, 62 eligible patients with multi-level cervical degenerative disease were treated, and the clinical data were reviewed. Of 62 patients, 32 underwent anterior cervical discectomy and fusion with Cage alone (group A), and 30 underwent anterior cervical discectomy and fusion with plate fixation (group B). Both groups showed no significant difference in gender, age, disease duration, lesion types, and affected segments (P>0.05), it had comparability. Clinical outcomes were assessed using Japanese Orthopedic Association (JOA) score and visual analogue scale (VAS) score; the fused segment height, subsidence rates of Cages, global cervical lordosis, and fusion rates were also compared. ResultsThe operation time of group B[(109.7±11.2) minutes] was significantly more than group A[(87.8±6.9) minutes] (t=-2.259, P=0.037). Primary healing of incisions was obtained in all patients of 2 groups. All patients were followed up; the follow-up period ranged from 8 to 27 months (mean, 15.8 months) in group A, and from 9 to 28 months (mean, 16.4 months) in group B. There was no complication and internal fixation failure. The JOA score and VAS score were significantly improved at last follow-up when compared with preoperative scores in 2 groups (P<0.05). According to Robinson standard for axial symptom severity, the results were excellent in 20 cases, good in 9, fair in 2, and poor in 1, with an excellent and good rate of 90.63% in group A; the results were excellent in 19 cases, good in 7, fair in 3, and poor in 1, with an excellent and good rate of 86.67% in group B; and no significant difference was found between 2 groups (χ2=0.765, P=0.382). The fused segment height at immediate after operation and at last follow-up and global cervical lordosis at last follow-up were significantly improved when compared with preoperative ones in 2 groups (P<0.05). There was no significant difference (P>0.05) between groups A and B in the Cage subsidence height[(1.4±0.9) mm vs. (1.2±1.6) mm], Cage subsidence rate[9.52% (8/84) vs. 7.59% (6/79)], and fusion rate[95.24% (80/84) vs. 96.20% (76/79)]. ConclusionAnterior cervical discectomy and fusion with Cage alone can obtain good clinical results and radiologic indexes, avoid plate-related complications and reduce operation time. It is a safe and effective surgical option in the treatment of multi-level cervical degenerative disease.
Percutaneous pulmonary puncture guided by computed tomography (CT) is one of the most effective tools for obtaining lung tissue and diagnosing lung cancer. Path planning is an important procedure to avoid puncture complications and reduce patient pain and puncture mortality. In this work, a path planning method for lung puncture is proposed based on multi-level constraints. A digital model of the chest is firstly established using patient's CT image. A Fibonacci lattice sampling is secondly conducted on an ideal sphere centered on the tumor lesion in order to obtain a set of candidate paths. Finally, by considering clinical puncture guidelines, an optimal path can be obtained by a proposed multi-level constraint strategy, which is combined with oriented bounding box tree (OBBTree) algorithm and Pareto optimization algorithm. Results of simulation experiments demonstrated the effectiveness of the proposed method, which has good performance for avoiding physical and physiological barriers. Hence, the method could be used as an aid for physicians to select the puncture path.
In deep learning-based image registration, the deformable region with complex anatomical structures is an important factor affecting the accuracy of network registration. However, it is difficult for existing methods to pay attention to complex anatomical regions of images. At the same time, the receptive field of the convolutional neural network is limited by the size of its convolution kernel, and it is difficult to learn the relationship between the voxels with far spatial location, making it difficult to deal with the large region deformation problem. Aiming at the above two problems, this paper proposes a cascaded multi-level registration network model based on transformer, and equipped it with a difficult deformable region perceptron based on mean square error. The difficult deformation perceptron uses sliding window and floating window techniques to retrieve the registered images, obtain the difficult deformation coefficient of each voxel, and identify the regions with the worst registration effect. In this study, the cascaded multi-level registration network model adopts the difficult deformation perceptron for hierarchical connection, and the self-attention mechanism is used to extract global features in the basic registration network to optimize the registration results of different scales. The experimental results show that the method proposed in this paper can perform progressive registration of complex deformation regions, thereby optimizing the registration results of brain medical images, which has a good auxiliary effect on the clinical diagnosis of doctors.
Skin cancer is a significant public health issue, and computer-aided diagnosis technology can effectively alleviate this burden. Accurate identification of skin lesion types is crucial when employing computer-aided diagnosis. This study proposes a multi-level attention cascaded fusion model based on Swin-T and ConvNeXt. It employed hierarchical Swin-T and ConvNeXt to extract global and local features, respectively, and introduced residual channel attention and spatial attention modules for further feature extraction. Multi-level attention mechanisms were utilized to process multi-scale global and local features. To address the problem of shallow features being lost due to their distance from the classifier, a hierarchical inverted residual fusion module was proposed to dynamically adjust the extracted feature information. Balanced sampling strategies and focal loss were employed to tackle the issue of imbalanced categories of skin lesions. Experimental testing on the ISIC2018 and ISIC2019 datasets yielded accuracy, precision, recall, and F1-Score of 96.01%, 93.67%, 92.65%, and 93.11%, respectively, and 92.79%, 91.52%, 88.90%, and 90.15%, respectively. Compared to Swin-T, the proposed method achieved an accuracy improvement of 3.60% and 1.66%, and compared to ConvNeXt, it achieved an accuracy improvement of 2.87% and 3.45%. The experiments demonstrate that the proposed method accurately classifies skin lesion images, providing a new solution for skin cancer diagnosis.