To compare the capacity of isolated medial or lateral meniscus allograft transplantation (MAT) and isolated anterior cruciate ligament (ACL) reconstruction (ACLR) to reduce pre-operative knee laxity in vivo. Isolated MAT was hypothesized to restore knee stability comparably to isolated ACLR. All surgical procedures performed with intraoperative navigation were retrospectively analyzed. Patients with post-meniscectomy syndrome and intact ACL undergoing medial or lateral MAT (MAT-M and MAT-L groups) were matched 1:1 by age and sex to patients with intact menisci undergoing ACLR (ACLR-M and ACLR-L groups, respectively). Intraoperative anteroposterior (AP), internal/external rotation, varus/valgus and pivot-shift (PS) laxity were quantified using the navigation system before and after surgery. Repeated-measures ANOVA was used to compare laxity between groups across pre- and post-operative states. Statistical significance was set at p < 0.05. From a total of 232 patients, 36 were selected: 8 in the MAT-M group, 8 in the ACLR-M group, 10 in the MAT-L group and 10 in the ACLR-L group. MAT-M reduced pre-operative AP laxity with no statistically significant difference compared to ACLR, across all measured parameters, including AP translation at 30° and 90° of flexion, internal/external rotation and varus-valgus. Conversely, ACLR was a significantly more effective stabilizer compared to MAT-L, demonstrating a greater reduction in AP laxity at 30° (43.2% vs. 37.2%, p = 0.045) and 90° of flexion (62.6% vs. 36.3%, p = 0.007), and PS area (62.5% vs. 32.5%, p = 0.002). In patients with chronic post-meniscectomy syndrome, isolated MAT-M restored AP knee stability comparably to isolated ACLR, suggesting that the medial meniscus acts as a primary stabilizer in this population. In contrast, isolated MAT-L was significantly less effective than ACLR in controlling AP and PS laxity, despite significantly reducing it. These findings highlight the critical, compartment-specific biomechanical role of the menisci. Level III.

Introduction AI techniques like convolutional neural networks (CNN), deep learning (DL), and neural networks (NN) have made it easier to automatically extract important clinical data for glioma post-treatment monitoring and surgical planning. Objective To systematically review and analyze the role of AI/ML models in the surgical planning of LGG. Methodology A rigorous and comprehensive systematic literature search was conducted across PubMed, Scopus, Web of Science Advance, ArXiV, and Embase (Ovid) databases from inception to July 14, 2025. Articles related to the utility of ML models in the surgical planning of LGG were included. Results Our review included eight studies in both preoperative and intraoperative settings with variation in the type of AI applied, such as tumor segmentation, intraoperative neuro navigation, hyperspectral imaging, and surgical recommendation. Upon comparative analysis of mean DICE coefficients of the proposed models for segmentation, the DeepMedic CNN was found to have the highest DICE for tumor segmentation. With hyperspectral imaging, the use of MLP classifiers yields high accuracy; however, when taking into consideration the quality of tiles, DL methods outperform the classical methods by ~10%. Survival Probability using the Balanced Survival lasso-network (BSL), balanced individual treatment effect (BITES), and DeepSurv models: Difference in restricted mean survival time (DRMST) between the Consis group and In-consis group [4.75 (1.54-7.95)] for BSL, [3.81 (0.63–6.98)] for Deep Surv, and [3.76 (0.57–6.96)] for BITES. Conclusions AI/ML models have shown promising results in diagnostic and management approaches for glioma resection. Nonetheless, this is based on a small number of studies (n=8) and remain preliminary. Validating the findings in external datasets with a larger patient population would help enhance the predictive capacity of the existing models.

BackgroundAccurate classification and segmentation of brain tumors in MRI scans are essential for diagnosis and treatment planning. However, the heterogeneous morphology of brain tumors, including irregular shapes, sizes, and spatial variability, makes this task highly challenging. Traditional convolutional neural networks (CNNs) lack rotational and translational invariance, which limits their ability to generalize across different orientations.MethodsThis study introduces a geometric deep learning framework called Modified Special Euclidean (Mod-SE(2)), which integrates geometric priors to enhance spatial consistency and reduce reliance on data augmentation. By incorporating symmetry-preserving group convolutions and spatial priors, Mod-SE(2) improves the robustness in tumor classification (namely Mod-Cls-SE(2)) and segmentation (mentioned as Mod-Seg-SE(2)). Unlike conventional CNNs, geometric deep learning encodes roto-translation symmetry directly into the architecture. This addresses the spatial variability and orientation sensitivity that are common in MRI-based diagnostics. Mod-SE(2) was evaluated on three MRI datasets and two other medical image datasets for classification and segmentation tasks. It incorporates lifting layers, group convolutions, and feature recalibration. It was benchmarked against U-Net, NN U-Net, VGG16, VGG19, and ResNet architectures.ResultsMod-Cls-SE(2) achieved an average classification accuracy of 0.914, outperforming ResNet101 with 0.682, VGG16 with 0.705, and their variants. In the binary classification of five tumor types (AVM, Meningioma, Pituitary, Metastases, and Schwannoma) from the private dataset, the model achieved an accuracy of 0.935 and a precision of 0.960 for pituitary tumors and a precision of 0.96. For segmentation tasks, Mod-Seg-SE(2) achieved a dice coefficient of 0.9503 and an IoU of 0.9616 on the BraTS2020 dataset. This result exceeds those of U-Net and NN U-Net with dice scores of 0.797 and 0.815, respectively. The model also reduced inference time and demonstrated strong computational performance.ConclusionsMod-SE(2) uses geometric priors to improve the spatial consistency, efficiency, and interpretability in brain tumor analysis. Its symmetry-aware design enables better generalization across tumor shapes and outperforms traditional methods across all key metrics. The Mod-SE(2) CNN ensures accurate boundary delineation, supporting neurosurgical planning, intraoperative navigation, and downstream applications such as Monte Carlo-based radiotherapy simulations and PET-MRI co-registration. Future work will extend the model to 3D volumes and validate its clinical readiness.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12929-025-01213-y.

Robotic surgery has emerged as a key minimally invasive approach for gastrointestinal malignancies, stimulating substantial global research activity. This study employed bibliometric and visual methods to map the knowledge structure, evolutionary trajectory, research hotspots, and emerging trends in this field. We systematically retrieved relevant publications in this field from the Web of Science Core Collection over the past decade and conducted a visualization analysis. The findings delineate four major research hotspots in this field, including comparative effectiveness research against laparoscopy, technical refinement and standardization, perioperative outcome optimization, and the integration of artificial intelligence (AI) and deep learning. The field's focus has evolved from initial feasibility studies toward recent investigations involving AI, deep learning, risk prediction, enhanced recovery after surgery, and multidisciplinary integration. The comprehensive integration of AI and deep learning, particularly through predictive modeling and intraoperative navigation, represents a key direction for future research. This study provides valuable guidance and insights for shaping future research agendas and refining clinical practice in this rapidly advancing field.

Advances in the near-infrared Ⅱ for in vivo fluorescence imaging applications: A review.

Evidence-Based Guidelines for the Surgical Management of Degenerative Lumbar Spondylolisthesis.

Nested resolution mesh-graph CNN for automated extraction of liver surface anatomical landmarks.

Multimodal medical image fusion technology optimizes image content by integrating images from diverse modalities, such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Single Photon Emission Computed Tomography (SPECT), while retaining critical information. With the rapid advancements in medical imaging technology, single-modal approaches have limitations in capturing comprehensive anatomical or functional characteristics. As a result, researchers are increasingly turning to multimodal fusion methods to enhance diagnostic accuracy and provide richer data for classification, detection, and segmentation tasks. In particular, during the perioperative period, multimodal image fusion plays a crucial role in surgical planning, intraoperative navigation, and postoperative evaluation, enabling precise localization of lesions and improving clinical decision-making. This paper presents a survey of the latest literature on medical image fusion, covering three major approaches: traditional methods, model-based methods, and learning-based methods. It discusses the advantages and limitations of each approach, with a particular emphasis on traditional image processing techniques, model-based fusion methods, and the integration of emerging deep learning (DL) technologies. Comparative experimental analysis highlights performance differences among these methods in terms of information retention, computational efficiency, and clinical applicability. Finally, the paper reviews performance evaluation metrics for multimodal fusion and provides recommendations for future research to further promote the widespread adoption of this technology in clinical diagnostics and intelligent healthcare.

(Isocitrate dehydrogenase) IDH-mutant astrocytoma is classified as World Health Organization (WHO) grade 2-4 and is second only to IDH wild-type glioblastoma in the incidence of adult glioma. However, few studies use single-cell and spatial transcriptome sequencing to analyze its malignant progression. Intraoperative navigation and yellow fluorescence visualization were utilized to accurately isolate high-grade (WHO grade 3-4) and low-grade (WHO grade 2) samples of IDH-mutant astrocytoma for single-cell and spatial transcriptome sequencing. By combining single-cell, spatial transcriptome, The Cancer Genome Atlas (TCGA), and The Chinese Glioma Genome Atlas (CGGA) data, analyses of survival, enriched pathways, transcription factors, intercellular communication, differentiation trajectories, and immune response were performed to identify the characteristics of a unique subpopulation of high-grade IDH-mutant astrocytoma. Our single-cell RNA sequencing analysis identified a distinct subpopulation (Cluster 7) present in high-grade IDH-mutant astrocytoma, which was localized to the terminus of the pseudotime trajectory. Importantly, this cluster not only exhibited an immunosuppressive phenotype correlated with poor clinical prognosis, but also demonstrated significant enrichment in Developmental Biology and Calcium Signaling pathways. Furthermore, this subpopulation engaged in prominent ligand-receptor interactions, particularly through PTN_PTPRZ1 and MIF_CD74 pairs. Notably, comparative analysis revealed that high-grade astrocytoma displayed both quantitatively and qualitatively enhanced communication networks when compared to their low-grade counterparts. Our single-cell RNA sequencing analysis identifies a distinct tumor cell subpopulation present in high-grade (WHO grade 3-4) adult IDH-mutant astrocytoma. This cluster, which likely arises from malignant progression in adult astrocytoma, may provide new insights for developing therapeutic strategies against this clinically challenging disease.

With the rapid development of medical imaging technology, computer-assisted dynamic intraoperative navigation (CADIN) technology has been introduced into the field of oral and maxillofacial surgery due to its technological features of accurately localizing key anatomical structures during surgery. Registration is a key step in CADIN technology. In different application scenarios, the choice of the registration method directly determines the accuracy of the navigation feedback, which in turn affects the effectiveness and safety of the entire surgery. In this paper, by searching and analyzing the database of articles on the application of CADIN technology in the field of oral and maxillofacial surgery for the years 2019-2025. The inclusion criteria are the application, optimization and system design of CADIN technology in oral and maxillofacial surgery. After screening 1069 articles, 42 articles were finally included. An analysis of the articles included in the study revealed that trauma and facial reconstruction guided by CADIN technology are hot research topics in the field of CADIN technology in oral and maxillofacial surgery. There are few reports on the use of CADIN technology to guide the endodontic treatment. In addition, the largest number of studies performed the registration process using the markerless. A review of the literature reveals that CADIN technology has great potential for practical clinical application in the field of oral and maxillofacial surgery and that the selection of appropriate registration methods can improve the accuracy of oral and maxillofacial surgical procedures.

With the swift advancement of dental implant technology, precise and safe radiographic assessment has emerged as pivotal for ensuring treatment success. Despite their widespread use, traditional radiographic methods are fraught with limitations, including exposure to ionizing radiation, metal artifact interference, and inadequate soft tissue resolution. Magnetic Resonance Imaging (MRI) showcases distinctive advantages in the realm of dental implants, thanks to its radiation-free nature, exceptional soft tissue contrast, and capabilities for multi-parameter and multi-sequence imaging. This article comprehensively reviews the latest advancements in MRI applications pertaining to preoperative planning, intraoperative real-time navigation, and postoperative long-term follow-up (early diagnosis of peri-implantitis and osseointegration assessment) in dental implantology. By conducting a comparative analysis of the merits and demerits of MRI versus traditional radiographic methods, and integrating typical cases and clinical research data, the article delves into the core clinical significance of MRI in enhancing implant accuracy, mitigating surgical risks, and refining prognostic evaluations. While MRI still encounters challenges such as cost, artifact issues, and the need for professional interpretation in routine clinical practice within dental implants, with ongoing technological refinements and the accumulation of clinical evidence, it promises vast potential as a pivotal non-invasive and precise imaging modality in the field of dental implants.

ASO Author Reflections: Democratizing Intraoperative Navigation through Simple CAD-Based 3D Modeling in HPB Surgery.

Artificial intelligence (AI) is rapidly transforming the landscape of dental implantology by enhancing every stage of treatment, from diagnostics and digital planning to intraoperative navigation, outcome prediction, and long-term follow-up. This narrative review explores the current and emerging applications of AI technologies in implant dentistry, with a focus on machine learning, neural networks, and computer vision. It examines how AI is utilized in digital implant planning, surgical navigation, peri-implant disease monitoring, risk assessment, and the prediction of treatment outcomes such as peri-implantitis and implant failure. These innovations contribute to more efficient workflows, more personalized treatment strategies, and improved cost-effectiveness of care. Finally, future perspectives and educational implications of AI integration in clinical implantology are discussed.

Background: Orthognathic surgery has undergone a significant technological evolution. Digital tools such as virtual surgical planning (VSP), three-dimensional (3D) printing, and computer-aided design/computer-aided manufacturing (CAD/CAM) have become central to the specialty, promising greater precision and predictability. Objective: To synthesize the scientific evidence on new techniques in orthognathic surgery, evaluating their methodologies, clinical outcomes, accuracy, and limitations. Methods: An integrative literature review was conducted in the PubMed/MEDLINE, ScienceDirect, and PMC databases. The search included articles published in the last 20 years using descriptors such as "orthognathic surgery," "virtual surgical planning," "3D printing," "computer-aided surgery," and "surgery-first approach." A total of 22 articles, including reviews, prospective studies, and systematic reviews, were selected. The analysis focused on the description of new techniques, clinical outcomes, and methodological quality, with a qualitative assessment of the level of evidence. Results: The main innovations identified were VSP, 3D-printed surgical guides and custom plates, the surgery-first approach, piezosurgery, and intraoperative navigation. The evidence indicates that these technologies increase surgical accuracy, reduce operative time, and allow for predictable results. VSP shows mean linear deviations of approximately 1.0-1.5 mm. The surgery-first approach reduces total treatment time. However, limitations such as the learning curve, high cost, and challenges in soft tissue prediction persist. Conclusion: New techniques in orthognathic surgery, especially those based on digital workflows, represent a significant advance over conventional methods, offering greater accuracy and efficiency. Despite the promising results, there is a need for more randomized clinical trials to establish a higher level of evidence and standardize protocols.

The craniofacial skeleton is a complex three-dimensional structure, and major reconstructive cases remain challenging. We describe a synergistic approach combining intra-operative navigation, three-dimensionally (3D) printed skull models, and mixed reality (MR) to improve predictability in surgical outcomes. A patient with previously repaired bilateral cleft lip and palate, significant midfacial retrusion, and a large maxillary alveolar gap underwent segmental Le Fort I osteotomy and advancement. Preoperative virtual planning was performed, and reference templates were uploaded onto MR glasses. Intra-operatively, the MR glasses projected the templates as holograms onto the patient’s skull, guiding osteotomy line marking and validating bony segment movement, which was confirmed with conventional navigation. The 3D-printed skull model facilitated dissection and removal of intervening bony spicules. Preoperative planning proceeded seamlessly across software platforms. Osteotomy lines marked with MR showed good concordance with conventional navigation, and final segment positioning was accurately validated. Postoperative outcomes were satisfactory, with re-established occlusion and closure of the maxillary alveolar gap. The combined use of conventional navigation, 3D-printed models, and MR is feasible and allows safe integration of MR into complex craniofacial reconstruction while further validation of the technology is ongoing.

Introduction One of the most challenges in spinal deformity surgery is screw placement, which utilizes various methods and options for radiographic guidance, particularly computed tomography-based navigation (CT navigation). Discussions about the advantages and disadvantages of the technologies used determined the relevance of this study.The aim of this study was to evaluate the effectiveness of intraoperative CT navigation in the surgical treatment of patients with spinal deformities using systematic data from the scientific literature. Materials and Methods A literature search for studies evaluating the parameters of surgical interventions using CT navigation in spinal deformity surgery was conducted in Pubmed, EMBASE, ELibrary, and Google. The article type was a systematic review and meta-analysis, with a search depth of 10 years. The study was conducted in accordance with the PRISMA international guidelines for systematic reviews and meta‑analyses. Levels of evidence and strength of recommendations were assessed using the ACCO protocol. A total of 40 articles were found in the databases, with 11 more articles in their reference lists, 48 of which were full‑text articles. Eight studies met the inclusion criteria, and two more were added in the sample by agreement of the authors. The following parameters were determined for analysis: screw placement accuracy, malposition rate and complications, operative time, blood loss, reoperation rate, reference frame positioning, and radiation exposure. Results and discussion The analysis revealed the advantages of using intraoperative CT navigation for screw placement. CT navigation improves screw placement accuracy, does not increase surgical time, and does not reduce the effectiveness of deformity correction. Surgery time, blood loss, and radiation exposure with CT navigation are comparable to other methods. Positioning of one reference frame significantly reduces surgical time, does not affect screw placement accuracy, and does not require additional CT scanning, thereby reducing radiation exposure. To reduce radiation exposure, it is recommended to set a scanning mode with a reduced radiation dose. Conclusion CT navigation offers advantages in terms of screw placement accuracy, lower malposition rates and associated complications, and reduced reoperation rates. The high safety profile of the navigation system is due not only to the increased accuracy of screw placement but also to lower complication rates.

Liver abscess is a common digestive system disorder primarily caused by bacterial infection. Effective treatment often involves drainage, in which ultrasound technology is commonly used to guide puncture and drainage procedures. However, ultrasound has inherent limitations such as suboptimal visualization of abscess boundaries and adjacent vascular anatomy. Indocyanine green (ICG) fluorescence navigation and three-dimensional (3D) visualization are two intraoperative navigation technologies that, when integrated with ultrasound, overcome these constraints. This synergy improves localization accuracy and abscess size assessment, thereby reducing procedural complexity. This article details the case of a 61-year-old male patient hospitalized for a 4-day history of fever of unknown origin, ultimately diagnosed with a pyogenic liver abscess. Preoperative management included standardized antibiotic therapy. The patient subsequently underwent concurrent laparoscopic abscess fenestration and drainage with cholecystectomy. Intraoperative precision localization of the liver abscess was achieved through ultrasound-guided ICG fluorescence imaging integrated with 3D visualization technology, ensuring successful surgical completion. During the 3-month postoperative follow-up, the patient developed no complications. ICG fluorescence imaging and 3D visualization technologies demonstrate clinical utility in the management of liver abscesses, providing valuable insights for laparoscopic surgical intervention.

In hepato-pancreato-biliary (HPB) surgery, precise anatomic understanding is essential.1.Ann Hepatobiliary Pancreat Surg. 23:145-154;2.J Hepatobiliary Pancreat Sci. 30:851-862; In Japan, advanced HPB board certification requires preoperative schema drawing.3.J Hepatobiliary Pancreat Sci. 24:252-261 Although three-dimensional (3D) imaging improves preoperative recognition,4.Ann Gastroenterol Surg. 6:190-196;5.Ann Surg Oncol. 32:3539-3543;6.Ann Surg Oncol. 31:6567-6568; no widely adopted system exists for real-time intraoperative navigation.7.Ann Surg. 271:e4-e7;8.Int J Comput Assist Radiol Surg. 20:117-129;9.Gastroenterol Res Pract. 2021:9621323; At the authors' institution, schematic diagrams have been used, but they are time-consuming and limited in detail. To overcome these limitations, the authors developed a simple, low-cost intraoperative 3D navigation method using free computer-aided design (CAD) applications. The authors analyzed 32 HPB cases managed between January and August 2025. Computed tomography (CT) data were reconstructed into 3D images using REVORAS (Ziosoft Inc., Tokyo, Japan). Based on these images, schematic diagrams were created with Procreate (Savage Interactive Pty Ltd., Australia), and CAD models were generated with Fusion 360 (Autodesk Inc., San Francisco, CA, USA) and Shapr3D (Shapr3D Zrt., Budapest, Hungary). The creation times were measured and compared. Surgical approaches included open (n = 14), robotic (n = 16), and laparoscopic (n = 2) procedures comprising pancreatic (n = 18), hepatic (n = 13), and sarcoma (n = 1) resection. Both schematic diagrams and CAD models were created in all cases. The median creation time was significantly shorter for CAD models (7 min 25 s) than for diagrams (19 min 48 s) (p < 0.01). The CAD models enabled real-time anatomic sharing via tablets with TilePro integration in the da Vinci Surgical System (Intuitive Surgical, Sunnyvale, CA, USA). Intraoperative CAD-based navigation is simple, cost-effective, and time-efficient, with strong potential for widespread adoption and educational value in HPB surgery.

Abstract Intraoperative fluorescence navigation is one of the most efficient and minimally invasive approaches for eliminating metastatic tumors in situ and poses minimal postoperative adverse effects. Therefore, a strategy combining enzyme responsiveness with in situ self‐assembly of small molecules can offer a promising approach for the precise removal of tumors. In this study, a fluorescence probe IHQA is reported for detecting and removing poorly vascularized metastatic foci via fluorescence guidance. The design of IHQA relies on the introduction of the solid‐state fluorophore, 6‐chloro‐2‐(2‐hydroxyphenyl) quinazolin‐4(3H)‐one (HPQ), which operates via an excited‐state intramolecular proton transfer (ESIPT) mechanism and plays a key role in enabling self‐assembly. Using Aminopeptidase N (APN) as a model target, it is demonstrated that overexpressed endogenous APN can activate this probe in tumor cells, resulting in the formation of nanoparticles ( IHQA‐nano ) that are directly endocytosed into lysosomes and can be visualized over the long term by fluorescence imaging. Simultaneous enhancements in signal‐to‐background ratio (&gt;14) and in vivo duration (up to 72 h) enable real‐time, high‐sensitivity, high‐spatial‐resolution imaging, localization, and removal of very small (2 mm 3 ) metastatic foci. Overall, this method of tumor elimination via fluorescence intraoperative navigation is a potential tool for the real‐time diagnosis and navigation of different metastases.

Retrospective Cost-Analysis Study. The primary aim of this study was to determine the cost-effectiveness from a public payer's perspective between RAN, NAV, and FH. Robotic-assisted navigation (RAN) and image-guided intraoperative navigation (NAV) are associated with higher pedicle screw placement accuracy and lower complication rates than freehand (FH) technique to treat idiopathic scoliosis. However, RAN and NAV are underutilized and payer coverage remains limited. A Markov decision-analysis model for a cost-utility analysis of FH/NAV/RAN for patients with IS was created, and a probability sensitivity analysis was performed. Probabilities of health states, associated reimbursement costs, and quality-adjusted life years (QALYs) were estimated from literature. For each technique, incremental cost-utility ratio (ICURs), net costs, incremental net monetary benefit, net monetary benefit, and QALYs were calculated. Cost-effectiveness acceptability (CEA) curve analysis was performed by varying WTPT between $10,000 to $250,000. Deterministic sensitivity analysis (DSA) was performed by varying probabilities, QALYs, and costs. For cost-effective treatment strategies, cost savings to payers, if present, were calculated over a 7-year horizon. When compared to FH technique, the ICUR of RAN ($10,672/QALY) and NAV (-$108,831/QALY) were below the societal willingness-to-pay threshold (WTPT) of $50,000. RAN was not more cost-effective than NAV (ICUR: $255,518/QALY) at a WTPT of $50,000. However, CEA demonstrated that RAN was the most cost-effective strategy for all WTPTs above $50,000. The mean cost of NAV per patient was lower than FH by $3610 (95% CI: $3419 - $3801; P < 0.001). Mean cost of RAN per patient was higher than FH by $527 (95% CI: $267 - $786; P < 0.001) and NAV by $4137 (95% CI: $3953 - 4320; P < 0.001). DSA demonstrated sensitivity to < 25% of variables. NAV and RAN are both more cost-effective than FH. NAV can save payers $45 million over 7 years. Payers should consider increasing reimbursement coverage for NAV and RAN. Level III.