Segmental Instrumentation Research Articles

Cataract-1K Dataset for Deep-Learning-Assisted Analysis of Cataract Surgery Videos

In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting in significant advancements in surgeons’ skills, operation room management, and overall surgical outcomes. However, the progression of deep-learning-powered surgical technologies is profoundly reliant on large-scale datasets and annotations. In particular, surgical scene understanding and phase recognition stand as pivotal pillars within the realm of computer-assisted surgery and post-operative assessment of cataract surgery videos. In this context, we present the largest cataract surgery video dataset that addresses diverse requisites for constructing computerized surgical workflow analysis and detecting post-operative irregularities in cataract surgery. We validate the quality of annotations by benchmarking the performance of several state-of-the-art neural network architectures for phase recognition and surgical scene segmentation. Besides, we initiate the research on domain adaptation for instrument segmentation in cataract surgery by evaluating cross-domain instrument segmentation performance in cataract surgery videos. The dataset and annotations are publicly available in Synapse.

Can surgical computer vision benefit from large-scale visual foundation models?

We investigate whether foundation models pretrained on diverse visual data could be beneficial to surgical computer vision. We use instrument and uterus segmentation in mini-invasive procedures as benchmarks. We propose multiple supervised, unsupervised and few-shot supervised adaptations of foundation models, including two novel adaptation methods. We use DINOv1, DINOv2, DINOv2 with registers, and SAM backbones, with the ART-Net surgical instrument and the SurgAI3.8K uterus segmentation datasets. We investigate five approaches: DINO unsupervised, few-shot learning with a linear decoder, supervised learning with the proposed DINO-UNet adaptation, DPT with DINO encoder, and unsupervised learning with the proposed SAM adaptation. We evaluate 17 models for instrument segmentation and 7 models for uterus segmentation and compare to existing ad hoc models for the tasks at hand. We show that the linear decoder can be learned with few shots. The unsupervised and linear decoder methods obtain slightly subpar results but could be considered useful in data scarcity settings. The unsupervised SAM model produces finer edges but has inconsistent outputs. However, DPT and DINO-UNet obtain strikingly good results, defining a new state of the art by outperforming the previous-best by 5.6 and 4.1 pp for instrument and 4.4 and 1.5 pp for uterus segmentation. Both methods obtain semantic and spatial precision, accurately segmenting intricate details. Our results show the huge potential of using DINO and SAM for surgical computer vision, indicating a promising role for visual foundation models in medical image analysis, particularly in scenarios with limited or complex data.

Surgery for Spinal Stenosis in Achondroplasia: Causes of Reoperation and Reduction of Risks.

Individuals with achondroplasia are prone to symptomatic spinal stenosis requiring surgery. Revision rates are thought to be high; however, the precise causes and rates of reoperation are unknown. The primary aim of this study is to investigate the causes of reoperation after initial surgical intervention in individuals with achondroplasia and spinal stenosis. In addition, we report on surgical techniques aimed at reducing the risks of these reoperations. A retrospective review was conducted over an 8-year period of all patients with achondroplasia at a single institution that serves as a large referral center for patients with skeletal dysplasias. Patients with achondroplasia who underwent spinal surgery for stenosis were identified and the need for revision surgery was studied. Data collected included demographic, surgical, and revision details. Fisher exact test was used to determine if an association existed between construct type and the need for revisions. Thirty-three of the 130 (22%) patients with achondroplasia required spinal stenosis surgery. Twenty-four individuals who met the criteria were selected for analysis. The initial spine surgery was at an average age of 18.7 years (SD: 10.1y). Nine patients (38%) required revision surgeries, and 3 required multiple revisions. Five of 9 (56%) of the revisions had primary surgery at an outside institution. Revision surgeries were due to caudal pseudarthrosis (the distal instrumented segment) (8), proximal junctional kyphosis (PJK) (7), and new neurological symptoms (7). There was a significant association found between construct type and the need for revision ( P =0.0111). The pairwise comparison found that short fusions were significantly associated with the need for revision compared with the interbody group ( P =0.0180). PJK was associated with short fusions when compared with the long fusion group ( P =0.0294) and the interbody group ( P =0.0300). Caudal pseudarthrosis was associated with short fusions when compared with the interbody group ( P =0.0015). Multivariate logistic regression found long fusion with an interbody was predictive of and protective against the need for revision surgery ( P =0.0246). To date, none of the initial cases that had long fusions with caudal interbody required a revision for distal pseudarthrosis. In patients with achondroplasia, the rate of surgery for spinal stenosis is 22% and the risk of revision is 38% and is primarily due to pseudarthrosis, PJK, and recurrent neurological symptoms. Surgeons should consider discussing spinal surgery as part of the patient's life plan and should consider wide decompression of the stenotic levels and long fusion with the use of an interbody cage at the caudal level in all patients to reduce risks of revision. Level IV-Retrospective case series.

Clinical outcome of posterior lumbar interbody fusion combined with Ponte osteotomy for reconstruction of coronal sagittal plane balance in degenerative scoliosis

Objective: To evaluate the clinical efficacy of posterior lumbar interbody fusion combined with Ponte osteotomy in the treatment of patients with degenerative scoliosis. Methods: The medical records and imaging data of degenerative scoliosis in department of orthopedics, Peking Union Medical College Hospital from 2013 to 2022 were retrospectively collected, and the shortest follow-up time was 1 year. A total of 38 patients were included, including 13 males and 25 females, aged 50-87(65.6±10.9) years old.The follow-up was12-119(43±20) months. Standing position full spine anteroposterior lateral X-ray examinations were performed on all patients preoperatively, postoperatively, and at latest follow-up. The length of hospital stay, complications, operation time, blood loss, instrumented segment, fusion segmen were recorded. The clinical scores and coronasagittal imaging indicators at three time points were compared. Results: The operation time was (274.5±70.5)min, and intraoperative blood loss was (619.2±93.5)ml. The coronal vertical axis was improved from (2.9±1.8)cm preoperatively to (1.2±1.0)cm postoperatively. The preoperative coronal Cobb angle was 16.6°±9.9° and the immediate postoperative correction was 6.4°±4.0°(t=-6.83, P<0.001). The difference was statistically significant (t=-6.12, P<0.001). The coronal Cobb Angle at the last follow-up was 5.7°±3.7°, and there was no significant orthopaedic loss at the last follow-up (t=-6.12, P<0.001).The sagittal vertical axis decreased from (5.6±3.9)cm preoperatively to (3.2±2.5) cm immediately after operation (t=-6.83,P<0.001), and was well maintained at the last follow-up[(2.7±1.8) cm,t=-7.77,P<0.001]. Lumbar lordosis increased from 21.8°±10.2° preoperatively to 35.8°±8.3° postoperatively(t=12.01, P<0.001)and 40.1°±8.6° at last follow-up(t=-10.21, P<0.001). Oswestry disability score (ODI score), visual analogue score (VAS) low back pain score and VAS leg pain score were also lower after surgery than before surgery (all P<0.05). Conclusion: Posterior lumbar interbody fusion combined with Ponte osteotomy can significantly improve the coronal and sagittal plane deformity and postoperative functional score in adult patients with degenerative scoliosis.

SurgNet: Self-Supervised Pretraining With Semantic Consistency for Vessel and Instrument Segmentation in Surgical Images.

Blood vessel and surgical instrument segmentation is a fundamental technique for robot-assisted surgical navigation. Despite the significant progress in natural image segmentation, surgical image-based vessel and instrument segmentation are rarely studied. In this work, we propose a novel self-supervised pretraining method (SurgNet) that can effectively learn representative vessel and instrument features from unlabeled surgical images. As a result, it allows for precise and efficient segmentation of vessels and instruments with only a small amount of labeled data. Specifically, we first construct a region adjacency graph (RAG) based on local semantic consistency in unlabeled surgical images and use it as a self-supervision signal for pseudo-mask segmentation. We then use the pseudo-mask to perform guided masked image modeling (GMIM) to learn representations that integrate structural information of intraoperative objectives more effectively. Our pretrained model, paired with various segmentation methods, can be applied to perform vessel and instrument segmentation accurately using limited labeled data for fine-tuning. We build an Intraoperative Vessel and Instrument Segmentation (IVIS) dataset, comprised of ~3 million unlabeled images and over 4,000 labeled images with manual vessel and instrument annotations to evaluate the effectiveness of our self-supervised pretraining method. We also evaluated the generalizability of our method to similar tasks using two public datasets. The results demonstrate that our approach outperforms the current state-of-the-art (SOTA) self-supervised representation learning methods in various surgical image segmentation tasks.

Will coronal alignment postoperatively be deteriorating in adult spinal deformity after long-fusion surgery?

BackgroundTo investigate whether the coronal alignment (CA) will deteriorate, and identify the risk factors for coronal malalignment (CM) developing in adult spinal deformity (ASD) after long-fusion surgery.MethodsA multi-center retrospective study was performed, which included a total of 161 ASD patients who had undergone the surgical procedure of long-fusion (≥ 5 vertebras) with instrumentations in three medical centers. All of the participants were retrospectively reviewed, and subsequently assigned into the consistency group (C7 plumb line (C7PL) shifting towards the convex side of the main curve), and the opposition group (C7PL shifting towards the concave side). CM was considered if the coronal balance distance (CBD) being over 30 mm. A Kaplan–Meier curve and log-rank test were used to analyze the differences in CM-free survival during follow-up. Multivariate analysis via a Cox proportional hazards test was used to analyze the risk factors.ResultsPatients showing CM equaled 35 (21.7%) at the pre-operation, and that increased significantly up to 51 (31.7%) at the final follow-up (P = 0.04). In the consistency group, the incidence of CM at the final follow-up was much higher than that preoperatively (35:16, P = 0.002). CM-free survival time decreased significantly in patients with larger CBD correction, pelvic fixation and more instrumented segments, respectively, during follow-up (P < 0.05, log-rank test). Age ≥ 60 years, the consistency CA, pelvic fixation, CBD-correction ≥ 30 mm and fixed-vertebra ≥ 8 were risk factors for CM happening after surgery using multivariate regression analysis (P < 0.05).ConclusionsThe coronal alignments in ASD patients underwent long-fusion surgeries may deteriorate during follow-up, for which the risk factors include the consistency CA, age ≥ 60, fixed-vertebra ≥ 8, CBD-correction ≥ 30 mm and pelvic fixation.

SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation

The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised image segmentation. To apply SAM to surgical instrument segmentation, a common approach is to locate precise points or boxes of instruments and then use them as prompts for SAM in a zero-shot manner. However, we observe two problems with this naive pipeline: (1) the domain gap between natural objects and surgical instruments leads to inferior generalisation of SAM; and (2) SAM relies on precise point or box locations for accurate segmentation, requiring either extensive manual guidance or a well-performing specialist detector for prompt preparation, which leads to a complex multi-stage pipeline. To address these problems, we introduce SurgicalSAM, a novel end-to-end efficient-tuning approach for SAM to effectively integrate surgical-specific information with SAM’s pre-trained knowledge for improved generalisation. Specifically, we propose a lightweight prototype-based class prompt encoder for tuning, which directly generates prompt embeddings from class prototypes and eliminates the use of explicit prompts for improved robustness and a simpler pipeline. In addition, to address the low inter-class variance among surgical instrument categories, we propose contrastive prototype learning, further enhancing the discrimination of the class prototypes for more accurate class prompting. The results of extensive experiments on both EndoVis2018 and EndoVis2017 datasets demonstrate that SurgicalSAM achieves state-of-the-art performance while only requiring a small number of tunable parameters. The source code is available at https://github.com/wenxi-yue/SurgicalSAM.

Biomechanical comparison of static and dynamic cervical plates in terms of the bone fusion, tissue degeneration, and implant behavior

IntroductionUsing an anterior cervical fixation device in the anterior cervical discectomy and fusion (ACDF) has evolved to various systems of static and dynamic cervical plates (SCP and DCP). Dynamic cervical plates have been divided into three categories: the rotational (DCP-R), translational (DCP-T), and hybrid (DCP-H) joints. However, little studies have been devoted to systematically investigate the biomechanical differences of dynamic cervical plates.Materials and methodsThe biomechanical tests of load-deformation properties and failure modes between the SCP and DCP systems are implemented first by using the UHMWPE blocks as the vertebral specimens. The CT-based C2-C7 model simulates the strategies of cervical plate in ACDF surgery is developed with finite-element analyses. One intact, one SCP and two DCP systems are evaluated for their biomechanical properties of bone fusion and tissue responses.ResultsIn the situation of biomechanical test, The mean values of the five ACDSP constructs are 393.6% for construct stiffness (p < 0.05) and 183.0% for the first yielding load (p < 0.05) less than those of the SCP groups, respectively. In the situation of finite-element analysis, the rigid-induced ASD is more severe for the SCP, followed by the DCP-H, and the DCP-R is the least.Discussion and conclusionsConsidering the degenerative degree of the adjacent segments and osteoporotic severity of the instrumented segments is necessary while using dynamic system. The mobility and stability of the rotational and translational joints are the key factors to the fusion rate and ASD progression. If the adjacent segments have been degenerative, the more flexible system can be adopted to compensate the constrained mobility of the ACDF segments. In the situation of the osteoporotic ACDF vertebrae, the stiffer system is recommended to avoid the cage subsidence.

Vertebral body tethering for adolescent idiopathic scoliosis: a review.

Adolescent Idiopathic Scoliosis (AIS) remains the most common type of pediatric scoliosis, mostly affecting children between ages 10 and 18. Vertebral body tethering (VBT) offers a non-fusion alternative to the gold standard spinal fusion that permits flexibility and some growth within instrumented segments. This article will serve as a comprehensive literature review of the current state-of-the-art of VBT in relation to radiographic and clinical outcomes, complications, and the learning curve associated with the procedure. A systematic literature review was conducted on PubMed, Scopus, and Web of Science from April 2002 to December 2022. Studies were included if they discussed VBT and consisted of clinical studies in which a minimum 2-years follow-up was reported, and series that included anesthetic considerations, learning curve, and early operative morbidity. Forty-nine studies spanning the period from April 2002 to December 2022 were reviewed. This article illustrates the potential benefits and challenges of the surgical treatment of AIS with VBT and can serve as a basis for the further study and refinement of this technique ideally as a living document that will be updated regularly.

Revisiting instrument segmentation: Learning from decentralized surgical sequences with various imperfect annotations.

This paper focuses on a new and challenging problem related to instrument segmentation. This paper aims to learn a generalizable model from distributed datasets with various imperfect annotations. Collecting a large-scale dataset for centralized learning is usually impeded due to data silos and privacy issues. Besides, local clients, such as hospitals or medical institutes, may hold datasets with diverse and imperfect annotations. These datasets can include scarce annotations (many samples are unlabelled), noisy labels prone to errors, and scribble annotations with less precision. Federated learning (FL) has emerged as an attractive paradigm for developing global models with these locally distributed datasets. However, its potential in instrument segmentation has yet to be fully investigated. Moreover, the problem of learning from various imperfect annotations in an FL setup is rarely studied, even though it presents a more practical and beneficial scenario. This work rethinks instrument segmentation in such a setting and propose a practical FL framework for this issue. Notably, this approach surpassed centralized learning under various imperfect annotation settings. This method established a foundational benchmark, and future work can build upon it by considering each client owning various annotations and aligning closer with real-worldcomplexities.

FUSIONLESS ALL PEDICLE SCREWS POSTERIOR DEFORMITY CORRECTION IN AIS IMMATURE PATIENTS PERMITS RESTORATION OF THE NORMAL VERTEBRAL MORPHOLOGY AND REMOVAL OF THE INSTRUMENTATION ONCE BONE MATURITY IS REACHED

The aim of this study was to report the restauration of the normal vertebral morphology and the absence of curve progression after removal the instrumentation in AIS patients that underwent posterior correction of the deformity by common all screws construct whitout fusion. A series of 36 AIS immature patients (Risser 3 or less) were include in the study. Instrumentation was removed once the maturity stage was complete (Risser 5). Curve correction was assessed at pre and postoperative, before instrumentation removal, just post removal, and more than two years after instrumentation removal. Epiphyseal vertebral growth modulation was assessed by a coronal wedging ratio (WR) at the apical level of the main curve (MC). The mean preoperative coronal Cobb was corrected from 53.7°±7.5 to 5.5º±7.5º (89.7%) at the immediate postop. After implants removal (31.0±5.8 months) the MC was 13.1º. T5–T12 kyphosis showed a significant improvement from 19.0º before curve correction to 27.1º after implants removal (p&lt;0.05). Before surgery, WR was 0.71±0.06, and after removal WR was 0.98±0.08 (p&lt;0.001). At the end of follow-up, the mean sagittal range of motion (ROM) of the T12-S1 segment was 51.2±21.0º. SRS-22 scores improved from 3.31±0.25 preoperatively to 3.68±0.25 at final assessment (p&lt;0.001). In conclusion, fusionless posterior approach using a common all pedicle screws construct correct satisfactory scoliotic main curves and permits removal of the instrumentation once the bone maturity is reached. The final correction was highly satisfactory and an acceptable ROM of the previously lower instrumented segments was observed.

Video-Instrument Synergistic Network for Referring Video Instrument Segmentation in Robotic Surgery.

Surgical instrument segmentation is fundamentally important for facilitating cognitive intelligence in robot-assisted surgery. Although existing methods have achieved accurate instrument segmentation results, they simultaneously generate segmentation masks of all instruments, which lack the capability to specify a target object and allow an interactive experience. This paper focuses on a novel and essential task in robotic surgery, i.e., Referring Surgical Video Instrument Segmentation (RSVIS), which aims to automatically identify and segment the target surgical instruments from each video frame, referred by a given language expression. This interactive feature offers enhanced user engagement and customized experiences, greatly benefiting the development of the next generation of surgical education systems. To achieve this, this paper constructs two surgery video datasets to promote the RSVIS research. Then, we devise a novel Video-Instrument Synergistic Network (VIS-Net) to learn both video-level and instrument-level knowledge to boost performance, while previous work only utilized video-level information. Meanwhile, we design a Graph-based Relation-aware Module (GRM) to model the correlation between multi-modal information (i.e., textual description and video frame) to facilitate the extraction of instrument-level information. Extensive experimental results on two RSVIS datasets exhibit that the VIS-Net can significantly outperform existing state-of-the-art referring segmentation methods. We will release our code and dataset for future research (Git).

Application of deep learning for semantic segmentation in robotic prostatectomy: Comparison of convolutional neural networks and visual transformers.

Semantic segmentation is a fundamental part of the surgical application of deep learning. Traditionally, segmentation in vision tasks has been performed using convolutional neural networks (CNNs), but the transformer architecture has recently been introduced and widely investigated. We aimed to investigate the performance of deep learning models in segmentation in robot-assisted radical prostatectomy (RARP) and identify which of the architectures is superior for segmentation in robotic surgery. Intraoperative images during RARP were obtained. The dataset was randomly split into training and validation data. Segmentation of the surgical instruments, bladder, prostate, vas and seminal vesicle was performed using three CNN models (DeepLabv3, MANet, and U-Net++) and three transformers (SegFormer, BEiT, and DPT), and their performances were analyzed. The overall segmentation performance during RARP varied across different model architectures. For the CNN models, DeepLabV3 achieved a mean Dice score of 0.938, MANet scored 0.944, and U-Net++ reached 0.930. For the transformer architectures, SegFormer attained a mean Dice score of 0.919, BEiT scored 0.916, and DPT achieved 0.940. The performance of CNN models was superior to that of transformer models in segmenting the prostate, vas, and seminal vesicle. Deep learning models provided accurate segmentation of the surgical instruments and anatomical structures observed during RARP. Both CNN and transformer models showed reliable predictions in the segmentation task; however, CNN models may be more suitable than transformer models for organ segmentation and may be more applicable in unusual cases. Further research with large datasets is needed.

Finite Element Analysis of Bilateral Double Rod Constructs in Thoracolumbar Fractures.

To evaluate bilateral double rod contructs in thoracolumbar fractures in a Finite Element model MATERIAL and METHODS: A computed tomography of a 35-year old male have been chosen to create a vertebra model and 1/3 of the T12 was removed to create the burst fracture model. In model A, transpedicular polyaxial screws were inserted two levels above and two levels below the burst fracture. On each side the screws were connected with a single rod. In model B, the screws were connected with two rods on each side attached to two lateral connectors. A uniform 150 N axial load and 10 N/m torque was applied on the superior T10. ROM and von Mises stress nephrograms revealed that the bilateral double-rod construct is being the most rigid and that the force on the pedicle screws were significantly lower compared to model A. We believe that bilateral double-rod constructs for the stabilization of thoracolumbar fractures have a decreased load on pedicle screws and rods compared to the classic bilateral single rod stabilization system and can lower the risk of implant failure and the risk for secondary complications and revision surgery.

Pixel-Wise Contrastive Learning for Multi-Class Instrument Segmentation in Endoscopic Robotic Surgery Videos Using Dataset-Wide Sample Queues

Pixel-Wise Contrastive Learning for Multi-Class Instrument Segmentation in Endoscopic Robotic Surgery Videos Using Dataset-Wide Sample Queues

Structural and Pixel Relation Modeling for Semisupervised Instrument Segmentation From Surgical Videos

Structural and Pixel Relation Modeling for Semisupervised Instrument Segmentation From Surgical Videos

Preoperative and Postoperative Segmental and Overall Range of Motion in Patients Undergoing Lumbar Spinal Fusion Using HA-Infused PEEK and HA-Treated Titanium Alloy Interbody Cages.

Retrospective observational radiographic analysis. Determine how single level lumbar interbody fusion (LIF) alters segmental range of motion (ROM) at adjacent levels and decreases overall ROM. This study included 54 patients who underwent single-level anterior (ALIF, 39%), thoraco-LIF (TLIF, 26%), posterior LIF (PLIF, 22%), or lateral LIF (LLIF, 13%) (L2-3/L3-4/L4-5/L5-S1: 4%/13%/35%/48%). Segmental ROM from L1-2 to L5-S1 and the overall lumbar ROM (L1-S1) were assessed from preoperative and postoperative flexion-extension radiographs. K-means cluster analysis was used to identify ROM subgroups. The overall L1-S1 ROM decreased 14% (25.5 ± 20.4° to 22.0 ± 17.2°, P = .104) postoperatively. ROM at the fusion level decreased 77% (4.8 ± 5.0° to 1.1 ± 1.1°, P < .001). Caudal adjacent segment ROM decreased 12% (5.2 ± 5.7° to 4.6 ± 4.4°, P = .345) and cranially ROM increased 34% (4.3 ± 5.0° to 5.7 ± 5.7°, P = .05). K-cluster analysis identified 3 distinct clusters (P < .05). Cluster 1 lost more ROM and had less improvement in patient-reported outcomes measures (PROMs) than average. Cluster 2 had less ROM loss than average with worse PROMs improvement. Cluster 3 did not have changes in ROM and better improvement in PROMs than average. Successful fusion was verified in 96% of all instrumented segments with >6months follow-up (ROM <4°). Following single-level L IF, patients should expect a loss of 3.3°, or 14% of overall lumbar motion with increases in ROM of the cranial segment. However, specific clusters of patients exist that experience different relative changes in ROM and PROMs.

A lightweight segmentation network for endoscopic surgical instruments based on edge refinement and efficient self-attention.

In robot-assisted surgical systems, surgical instrument segmentation is a critical task that provides important information for surgeons to make informed decisions and ensure surgical safety. However, current mainstream models often lack precise segmentation edges and suffer from an excess of parameters, rendering their deployment challenging. To address these issues, this article proposes a lightweight semantic segmentation model based on edge refinement and efficient self-attention. The proposed model utilizes a lightweight densely connected network for feature extraction, which is able to extract high-quality semantic information with fewer parameters. The decoder combines a feature pyramid module with an efficient criss-cross self-attention module. This fusion integrates multi-scale data, strengthens focus on surgical instrument details, and enhances edge segmentation accuracy. To train and evaluate the proposed model, the authors developed a private dataset of endoscopic surgical instruments. It containing 1,406 images for training, 469 images for validation and 469 images for testing. The proposed model performs well on this dataset with only 466 K parameters, achieving a mean Intersection over Union (mIoU) of 97.11%. In addition, the model was trained on public datasets Kvasir-instrument and Endovis2017. Excellent results of 93.24% and 95.83% were achieved on the indicator mIoU, respectively. The superiority and effectiveness of the method are proved. Experimental results show that the proposed model has lower parameters and higher accuracy than other state-of-the-art models. The proposed model thus lays the foundation for further research in the field of surgical instrument segmentation.

Biomechanical characteristics of a novel interspinous distraction fusion device in the treatment of lumbar degenerative diseases: a finite element analysis

BackgroundA novel interspinous distraction fusion (ISDF) device has been used to treat lumbar degenerative diseases. As a minimally invasive technique, ISDF differs from the traditional interspinous process distraction devices. Currently, biomechanical studies on ISDF are rare.ObjectiveTo investigate the biomechanical properties of the ISDF device (BacFuse) which is used to treat lumbar degenerative diseases.MethodsThree-dimensional L3-L5 models were created. The models were divided into four groups: intact (M1), local decompression alone (M2), internal fixation alone (M3) and local decompression combined with internal fixation (M4), based on different surgical procedures. Local laminectomy was performed to resect the lower part of the L4 lamina and the upper part of the L5 lamina at the right lamina of L4/5 in the M2 and M4 groups. After meshing the models elements, Abaqus were used to perform the finite element (FE) analysis. The intervertebral range of motion (ROM) was measured during flexion, extension, left lateral bending, right lateral bending, left rotation and right rotation under a follower load of 400 N with a 7.5Nm moment. The distributions of disc and facet joint stresses were observed and recorded. Spinal vertebral stress was compared, and internal fixation device stress was observed.ResultsThe ROM of L4/5 in M2 increased in flexion, extension, left lateral bending, right lateral bending, left rotation and right rotation compared with that in M1. In all motion directions, the ROM at L4/5 decreased, and the ROM at L3/4 increased after implantation of the ISDF device in M3 and M4 groups. The disc stress and facet joint stresses in the instrumented segment decreased after implantation of the ISDF device. The spinous process loaded a certain amount of stress in M3 and M4 groups. The spikes of the internal fixation device were loaded with the maximum stress.ConclusionBacFuse exhibited a reduction in intervertebral ROM, as well as decreased stress on the intervertebral disc and facet joint, while also demonstrating a discernible impact on the upper adjacent segment.

Minimally Invasive Surgery for Posterior Spinal Instrumentation and Fusion in Adolescent Idiopathic Scoliosis: Current Status and Future Application.

The posterior minimally invasive spine surgery (MISS) approach-or the paraspinal muscle approach-for posterior spinal fusion and segmental instrumentation in adolescent idiopathic scoliosis (AIS) was first reported in 2011. It is less invasive than the traditionally used open posterior midline approach, which is associated with significant morbidity, including denervation of the paraspinal muscles, significant blood loss, and a large midline skin incision. The literature suggests that the MISS approach, though technically challenging and with a longer operative time, provides similar levels of deformity correction, lower intraoperative blood loss, shorter hospital stays, better pain outcomes, and a faster return to sports than the open posterior midline approach. Correction maintenance and fusion rates also seem to be equivalent for both approaches. This narrative review presents the results of relevant publications reporting on spinal segmental instrumentation using pedicle screws and posterior spinal fusion as part of an MISS approach. It then compares them with the results of the traditional open posterior midline approach for treating AIS. It specifically examines perioperative morbidity and radiological and clinical outcomes with a minimal follow-up length of 2 years (range 2-9 years).