Methods: Patients admitted and treated for one-level (L4/5 or L5/S1) low-grade spondylolisthesis were included in the study. A total of 135 patients were divided into the degenerative or lytic spondylolisthesis groups according to their condition (81 patients [degenerative group] vs. 54 patients [lytic group]). To assess the level of similarity in the radiological findings between low-grade lytic spondylolisthesis and dysplastic spondylolisthesis, the pedicle diameters and vertebral heights of the L4 and L5 vertebrae were measured on computed tomography images. Measurements were then converted to each vertebra’s ratio to reduce confounding factors among individuals. Results: The affected vertebra had a smaller sagittal pedicle diameter/transverse pedicle diameter ratio in the low-grade lytic spondylolisthesis group compared to the degenerative group, and the posterior vertebral height/anterior vertebral height ratio of L5 was smaller in the L5/S1 lytic spondylolisthesis group compared to the degenerative spondylolisthesis group. Conclusions Low-grade lytic spondylolisthesis and dysplastic spondylolisthesis demonstrated similar radiological findings. Hence, surgeons should be attentive to the morphology of the vertebral body and posterior column during preoperative planning for the treatment of low-grade lytic spondylolisthesis.

This systematic review summarizes existing evidence and outlines the benefits of artificial intelligence-assisted spine surgery. The popularity of artificial intelligence has grown significantly, demonstrating its benefits in computer-assisted surgery and advancements in spinal treatment. This study adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a set of reporting guidelines specifically designed for systematic reviews and meta-analyses. The search strategy used Medical Subject Headings (MeSH) terms, including “MeSH (Artificial intelligence),” “Spine” AND “Spinal” filters, in the last 10 years, and English— from January 1, 2013, to October 31, 2023. In total, 442 articles fulfilled the first screening criteria. A detailed analysis of those articles identified 220 that matched the criteria, of which 11 were considered appropriate for this analysis after applying the complete inclusion and exclusion criteria. In total, 11 studies met the eligibility criteria. Analysis of these studies revealed the types of artificial intelligence-assisted spine surgery. No evidence suggests the superiority of assisted spine surgery with or without artificial intelligence in terms of outcomes. In terms of feasibility, accuracy, safety, and facilitating lower patient radiation exposure compared with standard fluoroscopic guidance, artificial intelligence-assisted spine surgery produced satisfactory and superior outcomes. The incorporation of artificial intelligence with augmented and virtual reality appears promising, with the potential to enhance surgeon proficiency and overall surgical safety.

According to a recent systemic review, hallux valgus deformity has a prevalence rate of about 23% among adults aged 18 to 65 years. To date, more than 100 operative methods have been reported for the correction of hallux valgus deformity. For young female with mild to moderate hallux valgus deformity, minimally invasive surgery can be considered for aesthetic demands. Here, we report a case of a young female patient with mild hallux valgus deformity treated by minimally invasive surgery using intramedullary low profile plate fixation. This can be the favorable method for secure fixation of the osteotomy site and prevention of medial skin irritation symptoms derived from a sharp osteotomy margin.
Adult ; Congenital Abnormalities ; Female ; Hallux Valgus ; Hallux ; Humans ; Methods ; Minimally Invasive Surgical Procedures ; Osteotomy ; Prevalence ; Skin

Methods: This study was conducted in 134 patients with OVFs initially treated conservatively. The patients were split into two groups: 3-day and 7-day ABR. From the time of injury to 1, 4, and 12 weeks after injury, compression rate (CR) and local kyphotic angle (LKA) were assessed and compared between the two groups. Any complications such as pneumonia, deep vein thrombosis, delirium, and urinary tract infection known to be related to ABR were examined based on the electronic medical record. Results: Forty-four patients underwent ABR for 3 days and 90 underwent ABR for 7 days. There was no significant difference in CR and LKA between the two groups at the time of injury versus 1, 4, and 12 weeks after injury. The patients were divided into two groups: those who received a 3-day ABR and those who received a 7-day ABR. CR and LKA were measured and compared between the two groups from the time of damage to 1, 4, and 12 weeks after injury. The ABR-related complication rate was 43.4% in the 7-day ABR group and 22.7% in 3-day ABR group (p=0.02). The duration of hospital stay was significantly shorter in the 3-day ABR group (12.8 days) than in the 7-day group (16 days) (p=0.01). Conclusions Considering radiological outcomes, prognosis, complications, patient convenience, and economic impact, a 3-day ABR period is appropriate for the conservative treatment of OVFs.

Purpose: To report the surgical results of the parapatellar approach for AO/OTA 33-C distal femoral intra-articular fractures. Materials and Methods: Twenty-one patients with AO/OTA 33-C distal femoral intra-articular fracture were included. There were 11 cases of C2 and 10 cases of C3 fractures. The time of union and the coronal alignment were radiographically investigated. The complications related to surgery were clinically investigated, and a functional evaluation using the range of motion and Oxford knee score was performed to compare the surgical results according to fracture classification. Results: In all cases, sufficient articular exposure and anatomical reduction were achieved with the parapatellar approach. No cases of coronal malalignment, loss of reduction, and plate failure were noted. On the other hand, in four cases (19.0%), an autogenous bone graft was performed due to delayed union on the meta-diaphyseal fracture site. There were no differences in the radiological and clinical outcomes of the C2 and C3 fractures. The knee joint pain and Oxford knee score were poorer in the delayed union group than the normal union group. Conclusion The parapatellar approach is useful for achieving an anatomical reduction of the articular surface of the distal femur and minimally invasive plating technique. Although satisfactory surgical results could be obtained regardless of the degree of articular comminution, a study of the risk factors of delayed metaphyseal fusion may be necessary.

Ossification of the posterior longitudinal ligament (OPLL) is a significant spinal condition that can lead to severe neurological deficits. Recent advancements in machine learning (ML) and deep learning (DL) have led to the development of promising tools for the early detection and diagnosis of OPLL. This systematic review evaluated the diagnostic performance of ML and DL models and clinical implications in OPLL detection. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed/Medline and Scopus databases were searched for studies published between January 2000 and September 2024. Eligible studies included those utilizing ML or DL models for OPLL detection using imaging data. All studies were assessed for the risk of bias using appropriate tools. The key performance metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC), were analyzed. Eleven studies, comprising a total of 6,031 patients, were included. The ML and DL models demonstrated high diagnostic performance, with accuracy rates ranging from 69.6% to 98.9% and AUC values up to 0.99. Convolutional neural networks and random forest models were the most used approaches. The overall risk of bias was moderate, and concerns were primarily related to participant selection and missing data. In conclusion, ML and DL models show great potential for accurate detection of OPLL, particularly when integrated with imaging techniques. However, to ensure clinical applicability, further research is warranted to validate these findings in more extensive and diverse populations.

Ossification of the posterior longitudinal ligament (OPLL) is a significant spinal condition that can lead to severe neurological deficits. Recent advancements in machine learning (ML) and deep learning (DL) have led to the development of promising tools for the early detection and diagnosis of OPLL. This systematic review evaluated the diagnostic performance of ML and DL models and clinical implications in OPLL detection. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed/Medline and Scopus databases were searched for studies published between January 2000 and September 2024. Eligible studies included those utilizing ML or DL models for OPLL detection using imaging data. All studies were assessed for the risk of bias using appropriate tools. The key performance metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC), were analyzed. Eleven studies, comprising a total of 6,031 patients, were included. The ML and DL models demonstrated high diagnostic performance, with accuracy rates ranging from 69.6% to 98.9% and AUC values up to 0.99. Convolutional neural networks and random forest models were the most used approaches. The overall risk of bias was moderate, and concerns were primarily related to participant selection and missing data. In conclusion, ML and DL models show great potential for accurate detection of OPLL, particularly when integrated with imaging techniques. However, to ensure clinical applicability, further research is warranted to validate these findings in more extensive and diverse populations.

Ossification of the posterior longitudinal ligament (OPLL) is a significant spinal condition that can lead to severe neurological deficits. Recent advancements in machine learning (ML) and deep learning (DL) have led to the development of promising tools for the early detection and diagnosis of OPLL. This systematic review evaluated the diagnostic performance of ML and DL models and clinical implications in OPLL detection. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed/Medline and Scopus databases were searched for studies published between January 2000 and September 2024. Eligible studies included those utilizing ML or DL models for OPLL detection using imaging data. All studies were assessed for the risk of bias using appropriate tools. The key performance metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC), were analyzed. Eleven studies, comprising a total of 6,031 patients, were included. The ML and DL models demonstrated high diagnostic performance, with accuracy rates ranging from 69.6% to 98.9% and AUC values up to 0.99. Convolutional neural networks and random forest models were the most used approaches. The overall risk of bias was moderate, and concerns were primarily related to participant selection and missing data. In conclusion, ML and DL models show great potential for accurate detection of OPLL, particularly when integrated with imaging techniques. However, to ensure clinical applicability, further research is warranted to validate these findings in more extensive and diverse populations.

Methods: This study analyzed 432 open-mouth (odontoid) radiographic views of cervical spine X-ray images obtained from dataset repositories, which were used in developing ANN models based on the convolutional neural network theory. All the images contained diagnostic information, including 216 radiographic images of individuals with normal odontoid processes and 216 images of patients with acute odontoid fractures. The model classified each image as either showing an odontoid fracture or not. Specifically, 70% of the images were training datasets used for model training, and 30% were used for testing. KNIME’s graphic user interface-based programming enabled class label annotation, data preprocessing, model training, and performance evaluation. Results: The graphic user interface program by KNIME was used to report all radiographic X-ray imaging features. The ANN model performed 50 epochs of training. The performance indices in detecting odontoid fractures included sensitivity, specificity, F-measure, and prediction error of 100%, 95.4%, 97.77%, and 2.3%, respectively. The model’s accuracy accounted for 97% of the area under the receiver operating characteristic curve for the diagnosis of odontoid fractures. Conclusions The ANN models with the KNIME Analytics Platform were successfully used in the computer-assisted diagnosis of odontoid fractures using radiographic X-ray images. This approach can help radiologists in the screening, detection, and diagnosis of acute odontoid fractures.

Obesity presents significant challenges in spinal surgery, including higher rates of perioperative complications, prolonged operative times, and delayed recovery. Traditional open spine surgery often exacerbates these risks, particularly in patients with obesity, because of extensive tissue dissection and larger incisions. Endoscopic spine surgery (ESS) has emerged as a promising minimally invasive alternative, offering advantages such as reduced tissue trauma, minimal blood loss, lower infection rates, and faster recovery. This systematic review and meta-analysis aimed to evaluate the safety, efficacy, and outcomes of ESS techniques, including fully endoscopic and biportal endoscopic lumbar discectomy and decompression, in patients with obesity and lumbar spine pathologies. A comprehensive literature search of the PubMed/Medline, Embase, and Scopus databases yielded 2,975 studies published between 2000 and 2024, of which 10 met the inclusion criteria. The meta-analysis revealed significant improvements in pain relief (Visual Analog Scale) and functional outcomes (Oswestry Disability Index), with comparable results between patients with and without obesity. Patients who are obese experienced longer operative times and have a slightly higher risk of symptom recurrence; however, ESS demonstrated lower rates of wound infections, shorter hospital stays, and faster recovery than traditional surgery. These findings position ESS as a viable and effective option for managing lumbar spine conditions in patients with obesity, addressing obesity-related surgical challenges while maintaining favorable clinical outcomes. However, limitations such as study heterogeneity and the lack of randomized controlled trials highlight the need for further high-quality research to refine ESS techniques and optimize patient care in this high-risk population.