Search Results (499)

Artificial intelligence (AI) has become an indispensable tool in orthopedic surgery. It provides new methods to increase surgical precision, improve patient safety, and support personalized treatment plans. This review presents a comprehensive analysis of AI-assisted orthopedic surgery across three core domains. Based on 89 recent studies, this review organizes findings around a perception–decision–execution framework. It groups diverse AI applications into certain categories while highlighting the mutuality across domains. Perception systems have progressed from basic CNN-based segmentation models to advanced transformer architectures. They support multi-modal data fusion and enable uncertainty quantification. Decision systems have moved far beyond rigid rule-based methods and evolve into data-driven models that support surgical planning, accurate risk prediction and continuous outcome optimization. And execution systems have advanced from passive navigation tools to active robotic assistance systems with real-time adaptive capabilities. Beyond mapping technological advances, this review also identifies pivotal challenges that hinder clinical translation and concludes with a clear roadmap for future research, which marks closed-loop surgical assistance systems as the next key development direction. Building on these findings, this review illuminates the potential of AI-assisted orthopedic surgery and guides future research toward innovations that can be translated into clinical practice. Full article

Background: Esophagectomy remains the definitive curative treatment for esophageal cancer but is historically burdened by significant procedure-related morbidity. Anastomotic leakage (AL) is still the “Achilles’ heel” of esophageal surgery, serving as a primary benchmark for surgical quality due to its profound impact on patient recovery, healthcare costs, and long-term oncological outcomes. While surgical expertise and perioperative care have matured, reported AL rates remain persistently high. This necessitates a shift in focus from purely technical modifications toward integrated, data-driven preventive strategies. Purpose: Five years after our initial review, this update synthesizes the rapid evolution in AL prevention. We evaluate the transition from empirical surgical pragmatism to evidence-based protocols, integrating recent breakthroughs in real-time perfusion monitoring, prophylactic endoluminal technologies, and multidisciplinary patient optimization. This work provides a contemporary “roadmap” for navigating the complexities of esophageal reconstruction. Conclusions: The prevention of AL has evolved into a multimodal “bundle” that begins well before the index operation. This review highlights the critical shift toward quantitative perfusion assessment via indocyanine green fluorescence angiography, which is increasingly replacing subjective visual inspection as the standard for anastomotic site selection. We discuss the emerging role of gastric ischemic preconditioning as a biological strategy to enhance conduit vascularity, alongside the paradigm of proactive management using preemptive endoluminal vacuum therapy to mitigate septic sequelae in high-risk cases. Furthermore, we examine technical refinements in conduit construction and conditioning—focusing on the ‘tension-perfusion’ relationship—and the essential role of structured prehabilitation within enhanced recovery after surgery frameworks. While the quality of evidence remains heterogeneous, the move toward standardized reporting and objective monitoring marks a new era of precision in esophageal surgery. Full article

Minimally invasive parafascicular surgery (MIPS) represents a paradigm shift in the management of deep-seated brain tumors, enabling function-sparing resections previously limited to biopsy and/or medical therapy. Central to MIPS are structured frameworks guiding preoperative planning and intraoperative execution. The six-pillar concept—comprising imaging, navigation, atraumatic access, optics, resection, and postoperative care—provides a comprehensive approach to integrate advanced neuroimaging, tractography, tubular retractor systems, fluorescence-guided resection, and neuromonitoring to optimize functional outcomes. Five-point target-trajectory complex planning—craniotomy, outer radial corridor, inner radial corridor, target, and resection margins—translates preoperative imaging and functional mapping into a precise surgical trajectory, balancing maximal tumor resection with minimal disruption of eloquent brain structures. Preoperative assessment of tumor characteristics, vascular relationships, and cortical eloquence informs trajectory planning and intraoperative adjustments. A critical determinant of MIPS success is the intraoperative golden hour, referring to the high-risk period surrounding brain cannulation with a tubular retractor. Key principles include (1) precannulation system checks to ensure instrument readiness; (2) access injury prevention through optimized craniotomy sizing and sulcal preparation; (3) tubular-tumor targeting accuracy addressing brain and tubular translation, tumor displacement, and white-matter sleeves; and (4) intracranial pressure control strategies to minimize tissue strain and venous congestion. Overcoming this period enables a controlled resection phase guided by the above-mentioned surgical adjuncts. The six-pillar concept and five-point target-trajectory complex planning are the foundations of MIPS planning, whereas the intraoperative golden hour provides a roadmap for successful intraoperative delivery of the surgical plan. Full article

Reconstruction in head and neck surgery requires restoration of complex functions, including speech, swallowing, and breathing, while preserving as much facial form and patient identity as possible. Over the past decade, advances in preoperative digital planning, intraoperative technologies, and robotic platforms have reshaped reconstructive strategies, giving rise to the concept of hybrid reconstruction. Hybrid approaches integrate free tissue transfer with computer-aided design and manufacturing (CAD/CAM), virtual surgical planning, intraoperative navigation, and robot-assisted microsurgery to enhance precision, reproducibility, and functional outcomes. This narrative review examines the principles and applications of hybrid reconstruction in head and neck surgery with particular emphasis on osseous reconstruction of the mandible, maxilla, and midface. The roles of intraoperative navigation and robotic assistance as enabling tools are discussed, along with their potential benefits and current limitations. Functional and morphologic outcomes, patient-reported quality of life, and challenges related to cost, access, training, and evidence heterogeneity are critically reviewed. Hybrid reconstruction represents an advancement toward outcomes-driven, patient-centered care; however, thoughtful integration of emerging technologies and continued emphasis on rigorous outcome assessment are essential to guide responsible adoption in contemporary head and neck reconstructive surgery. Full article

Background/Objectives: Reconstruction of orbital floor fractures remains surgically challenging due to limited intraoperative visibility and complex anatomy. Inaccurate implant placement often leads to persistent complications and the need for a revision surgery. This study evaluated the clinical accuracy and re-operation rates of a preoperative 3D-printed model-assisted technique compared to the conventional intraoperative free-hand mesh bending method. Methods: A comparative ambispective study was conducted on 74 patients with isolated orbital floor fractures. The control group (n = 34, retrospective) underwent reconstruction using intraoperatively formed titanium meshes. In the study group (n = 40, prospective), patient-specific 3D-printed models, created by mirroring the healthy contralateral orbit, were used for preoperative mesh adaptation. Primary outcomes included the rate of revision surgery due to implant malposition, changes in orbital volume, and postoperative diplopia. Results: The 3D model group demonstrated a significantly lower rate of revision surgery compared to the control group. In the retrospective group, 5 patients (15%) required reoperation due to implant malposition, whereas no patients (0%) in the prospective 3D group required secondary intervention (p = 0.017). While both techniques effectively restored orbital volume, the 3D group showed greater volumetric precision with less variance. The mean volume difference in the affected orbit was 3078 ± 2204 mm³ in the control group, compared to 2390 ± 1893 mm³ in the study 3D group. At the 6-month follow-up, persistent diplopia was observed in 12% of the control group compared to only 3% in the study group. Conclusions: The use of in-house 3D-printed models for preoperative mesh forming significantly enhances surgical precision and eliminates the need for revision surgery due to implant malposition. This workflow offers a cost-effective, predictable, and accessible alternative to expensive patient-specific implants (PSIs) or intraoperative navigation systems, improving patient safety and long-term clinical outcomes. Full article

Artificial intelligence (AI) is increasingly transforming spine surgery, with expanding applications in diagnostics, intraoperative imaging, and surgical navigation. As the field advances toward greater precision and safety, machine learning (ML) and deep learning technologies are being integrated to augment surgeon expertise and optimize operative workflows. In particular, AI-driven innovations in image acquisition and navigation are reshaping intraoperative decision-making and technical execution. This narrative review provides an overview of AI applications relevant to intraoperative imaging and navigation in spine surgery. We begin by defining key concepts in AI, ML, and deep learning and briefly outline the historical evolution of AI within spine practice. We then examine current capabilities in image recognition and automated pathology detection, emphasizing their clinical relevance. Given the central role of imaging accuracy in modern navigation-assisted procedures, we review conventional acquisition platforms, including intraoperative computed tomography (CT) systems (e.g., O-arm, GE, Airo), surface-based registration to preoperative CT (Stryker, Medtronic), and optical surface mapping technologies (e.g., 7D Surgical). Emerging AI-optimized advancements are subsequently discussed, including low-dose intraoperative CT protocols, expanded scan windows, metal artifact reduction algorithms, integration of 2D fluoroscopy with preoperative CT datasets, and 3D reconstruction derived from 2D imaging. These developments aim to improve image quality, reduce radiation exposure, and enhance navigational accuracy. By synthesizing current evidence and technological progress, this review highlights how AI-enhanced imaging systems are redefining intraoperative spine surgery and shaping the future of precision-based care. The primary purpose of this review is to outline the applications of AI and its potential for perioperative and intraoperative optimization, including radiation exposure reduction, workflow streamlining, preoperative planning, robot-assisted surgery, and navigation. The secondary purpose is to define AI, machine learning, and deep learning within the medical context, describe image and pathology recognition, and provide a historical overview of AI in orthopedic spine surgery. Full article

Artificial intelligence (AI) is rapidly reshaping head and neck surgical oncology by augmenting decision-making across the full perioperative continuum. This state-of-the-art review aims to provide head and neck surgical oncologists with a conceptual framework for understanding and critically appraising AI tools entering clinical practice, summarizing how machine learning, deep learning, and generative AI are being integrated into contemporary surgical workflows. Preoperative applications include detection of occult nodal metastasis and extranodal extension. Intraoperative innovations include augmented reality-assisted navigation, real-time margin assessment, and improving visual clarity and tissue handling for robotic platforms. Postoperatively, AI can predict complications like free flap failure and oncologic outcomes. Large language models are being operationalized for clinician-facing applications such as documentation and inbox support, as well as patient-facing education. Despite promising results, broad clinical deployment remains limited by concerns about privacy, validation, reliability, safety, and ethics. Widespread adoption will require prospective clinical trials, robust governance, and human-centered workflows that ensure AI remains a safe, assistive copilot. Full article

Background/Objectives: Robot-assisted partial nephrectomy (RAPN) remains a technically demanding procedure, associated with a non-negligible risk of perioperative complications. This study aimed to assess the impact of preoperative planning and intraoperative navigation using patient-specific three-dimensional (3D) virtual model reconstructions on perioperative outcomes of RAPN. Methods: We analyzed 307 patients who underwent RAPN for renal tumors at a tertiary center between 2021 and 2024. Starting in 2023, 3D modeling (Medics3D) was integrated for selected cases (n = 69) and compared to a 2D-imaging control group (n = 238). The primary outcome was trifecta achievement, defined as the simultaneous presence of negative surgical margins, ≥90% preservation of preoperative eGFR at discharge, and absence of perioperative complications. Clamping strategies were categorized as on-clamp, selective/super-selective, or off-clamp. Mann–Whitney and Chi-squared tests compared the groups; multivariable logistic regression identified independent predictors of trifecta achievement. Results: Baseline characteristics were balanced between the 3D and control groups: median age (62 vs. 61 years, p = 0.5), BMI (28 vs. 26, p = 0.3), and eGFR (85 vs. 86 mL/min/1.73 m², p = 0.5). Median tumor size was 4.2 vs. 4.0 cm (p = 0.4), and RENAL complexity was comparable (p = 0.12). Selective or super-selective clamping was significantly more frequent in the 3D group (32% vs. 15%; p < 0.01). While WIT (17.5 vs. 18.5 min, p = 0.09) and complication rates (26% vs. 29%, p = 0.7) were similar, the 3D group showed a significantly lower rate of positive surgical margins (5% vs. 15%; p = 0.030). Trifecta achievement was significantly higher in the 3D group (51% vs. 32%; p = 0.004). On multivariable analysis, 3D modeling remained an independent predictor of trifecta achievement (OR 2.1, 95% CI 1.17–3.70; p = 0.013). Conclusions: The use of patient-specific 3D kidney reconstructions was associated with improved perioperative outcomes in patients undergoing RAPN. These findings support the integration of 3D modeling into routine surgical workflows to enhance operative precision and optimize patient outcomes. Full article

Background/Objectives: For patients with Ulcerative Colitis (UC) requiring surgical treatment, post-operative dietary management can pose significant challenges. TikTok is emerging as a popular social media platform for dissemination of health and nutrition information. The aim of this study is to analyse patient-generated content on TikTok regarding dietary management post-UC surgery, in order to identify recurring themes and highlight patient priorities. Methods: Relevant TikTok videos were identified through a systematic search. Search terms were developed by combining ‘diet UC’ or ‘nutrition UC’ with common UC surgical procedures. From each search term, the first 10 videos were screened. If a search produced fewer than 10 results, all identified videos were retrieved. Inclusion criteria were videos in English, and a strong indication that the content creator was diagnosed with UC and had undergone relevant surgery, and was providing nutrition recommendations. Thematic analysis of video transcripts was conducted using Braun and Clarke’s framework to identify common themes. Results: A total of 89 videos, created between 2021 and 2024, were found on the initial search, of which 12 duplicates were removed, and 77 videos were screened. Sixteen English language videos met the inclusion criteria and were analysed. Thematic analysis identified three overarching themes: (1) adaptive dietary progression in the post-surgical period, where patients described a phased approach to reintroducing foods post-surgery; (2) personalisation of diet, highlighting individualised strategies for symptom and hydration management; and (3) Emotional and social impact of dietary restrictions and modifications, including fear of food and social isolation. Conclusions: This thematic analysis offers an insight into how patients navigate the complex management of diet following UC surgery. It is important for clinicians to discuss the dietary information and online content patients are exposed to in relation to their condition. Additionally, clinical practice should evolve to embrace patient-centred, multidisciplinary approaches that validate lived experience, ensure consistent dietary guidance, and address the psychological burden of dietary restriction. Full article

Background: Digital surgery integrates advanced imaging, computational modeling, additive manufacturing, and intraoperative navigation technologies. Although widely explored, most platforms remain fragmented and lack regulatory cohesion. The B-onic Platform was conceived as a unified workflow that enables surgical planning, device personalization, and intraoperative navigation within a regulatory-compliant framework. Objective: This study aimed to present a comprehensive single-center clinical evaluation of the implementation of the B-onic Platform in a large single-center cohort, focusing on efficiency, patient safety, and surgeon-reported outcomes. Methods: A retrospective review of 308 consecutive surgical plans was performed at La Paz University Hospital (Madrid, Spain) between 2020 and 2024 and compared with institutional historical controls from 2018 to 2019. Procedures included maxillofacial surgery, traumatology, reconstructive surgery, and other specialties. The platform incorporated imaging-based CAD modeling, 3D-printed biomodels and guides, and immersive validation through the NavigatorPro XR module. Outcomes analyzed were preoperative planning time, operative duration, 30-day complication and rehospitalization rates, intraoperative blood loss, and surgeon-reported perception of anatomical understanding and intraoperative confidence. Results: Mean preoperative planning time was reduced by 34% (−42 h; 95% CI: −48 to −36 h; p < 0.01) compared with historical controls. Mean operative duration decreased from 226 ± 74 min to 181 ± 61 min (−45 min; 95% CI: −52 to −38 min; p < 0.001). The 30-day postoperative complication rate decreased from 12.9% to 10.7% (absolute reduction 2.2%; 95% CI: 0.2–4.1%; p = 0.037), while rehospitalization rates declined from 9.1% to 4.3% (p = 0.012). Mean length of hospital stay decreased from 6.8 ± 3.1 to 5.2 ± 2.3 days (p = 0.022), and intraoperative blood loss was reduced by 12–30% across specialties (p = 0.008). NavigatorPro XR halved validation time for guides and implants (71.8 ± 22.4 h vs. 35.6 ± 18.9 h; p < 0.001). Ninety-two percent of surveyed surgeons reported improved 3D anatomical understanding and enhanced intraoperative safety. Conclusions: The B-onic Platform has transitioned from a prototype to a consolidated system, integrated into routine practice with significant gains in efficiency, safety, and training value. These findings support the potential of the platform as a precision surgery model; however, further multicenter prospective studies are required to confirm scalability. Full article

Background/Objectives: Radiomics-based machine learning models have demonstrated high accuracy in differentiating benign from malignant orbital masses, with early studies suggesting performance comparable to expert radiologists. However, translation into clinical practice remains limited due to dataset constraints, including retrospective study designs, single-center cohorts, and underrepresentation of diverse patient populations. This review aims to evaluate the current evidence supporting radiomics in orbital disease while critically examining barriers to generalizability and equity across ophthalmology, otolaryngology, and plastic surgery. Methods: A narrative literature review was conducted to assess radiomics applications in orbital oncology and reconstruction. Studies evaluating diagnostic accuracy, margin assessment, postoperative surveillance, and surgical planning across ophthalmology, head and neck surgery, and reconstructive surgery were analyzed, with particular attention paid to dataset composition, validation strategies, and imaging standardization. Results: Radiomics models demonstrated high diagnostic performance in differentiating orbital tumors, optimizing surgical planning, and aiding postoperative monitoring. However, most studies relied on small, homogeneous datasets lacking racial, ethnic, and pediatric representation. External validation was uncommon, and imaging heterogeneity limited reproducibility. These deficiencies restrict the clinical translation of radiomics and risk exacerbating healthcare disparities, particularly among underrepresented populations. Conclusions: Radiomics holds promise as a precision medicine tool for orbital diagnosis, surgical navigation, and postoperative care. Nevertheless, its clinical adoption is constrained by dataset bias, lack of standardization, and limited prospective validation. Future progress requires multi-institutional, demographically diverse datasets and standardized imaging protocols to ensure equitable and generalizable implementation across specialties. Full article

Background and objectives: Accurate anatomical knowledge of the transverse dural venous sinuses (TS) is essential for safe neurosurgical procedures, particularly in resource-limited settings where advanced imaging modalities may be unavailable. Despite the TS’s clinical importance, detailed cadaveric studies focusing solely on its morphology are scarce. This study investigated the length, width, and shape of the TS in adult human cadavers, assessing anatomical dominance and morphological variations relevant to surgical planning. Methods: A descriptive, cross-sectional study was conducted on 32 formalin-fixed adult cadavers (20 male, 12 female) at the University of Pretoria in South Africa. The TS was examined bilaterally within the dura mater and the corresponding transverse sulcus. Lengths were measured using a string and a ruler to accommodate curvature, while widths at the origin, midpoint, and termination were measured using digital calipers. Statistical analyses included Shapiro–Wilk tests, paired t-tests, and intra-class correlation to determine significance and reliability. Results: The average TS length was 72.54 mm (left) and 70.23 mm (right), with no statistically significant differences between sides. Right-sided dominance in TS width was observed in 71.88% of cases. A significant narrowing at the midpoint, followed by widening at the termination, was consistently noted, especially in males. Differences between dural and bony groove widths suggested that sulcal impressions may not accurately reflect TS dimensions. Conclusions: The TS demonstrates significant morphological variability, including asymmetry and abrupt dimensional changes. These findings underscore the importance of direct anatomical reference for surgical navigation, particularly in low-resource settings lacking advanced imaging. Full article

Background/Objectives: Women’s health has historically lagged behind other medical specialties in transformative innovation, despite significant technological advances in adjacent fields. In this collection of papers, we examine the current state of innovation in women’s health and maternal–fetal medicine, identify barriers to transformation, and propose strategies for accelerating breakthrough developments. This paper presents an overview of multiple forces and their often-competing relationships that influence the environment in which advances in multiple areas of healthcare have had to navigate to enter mainstream practice. An understanding of these forces is essential to explain why some new technologies are readily deployed into clinical practice while others take many years to be adopted. Understanding the entire “echo-system” around any specific technology provides a much fuller understanding of how any individual advance can make its way into actual utilization. Methods: We synthesized current literature on innovation in women’s health, analyzing technological advances in artificial intelligence, precision medicine, non-invasive diagnostics, and surgical robotics. We examined patterns of innovation adoption and barriers to implementation across multiple domains. Results: Several key areas presented in this paper and the following show promise for transformative change: artificial intelligence (AI)-driven diagnostics achieving expert-level performance in prenatal screening, precision medicine approaches transforming genetic disease management, and non-invasive monitoring technologies revolutionizing maternal–fetal care. However, systemic barriers including regulatory complexity, liability concerns, and institutional inertia continue to limit widespread adoption of numerous breakthrough technologies. Conclusions: The convergence of multiple technological advances, particularly artificial intelligence and precision medicine, positions women’s health for unprecedented transformation. Success requires fostering innovation-ready environments, embracing systems-awareness approaches, and maintaining focus on human-centered care while leveraging technological capabilities with continual feedback and course corrections. Full article

Background/Objectives: This study investigates a surgical concept that restores constitutional bony alignment within predefined safety boundaries in primary total knee arthroplasty (TKA) using modern 3D navigation. The technique combines a standard knee implant with advanced navigation technology to achieve patient-specific alignment and recreate native joint mechanics. One-year outcome was evaluated to assess first clinical results. Methods: In this retrospective study, a consecutive series of 185 TKAs (171 patients) was analyzed. All patients underwent patient-specific restoration of constitutional alignment within predefined safety boundaries using a 3D navigation system and a standard knee arthroplasty implant. The clinical outcomes were assessed using the 2011 Knee Society Score (KSS), the Forgotten Joint Score (FJS-12), the UCLA Activity Scale, and a five-step Likert scale to evaluate satisfaction. Results: In a total of 87.6% of cases, the patients reported being either satisfied or very satisfied with their TKA. No patients reported strong dissatisfaction. The KSS demonstrated significant improvements in all subcategories (all p < 0.001). The FJS-12 increased significantly from a preoperative average of 32.5 points to 79.3 points postoperatively (p < 0.001). The mean UCLA activity score rose from 4.9 preoperatively to 6.6 postoperatively (p < 0.001). In 97.7% and 90.2% of cases, the femoral mechanical angle (FMA) and tibial mechanical angle (TMA) bone cuts were within ± 1° of the planned angles. A strong correlation was observed between the planned and verified bone cuts for the FMA (ρ = 0.939) and the TMA (ρ = 0.875). Conclusions: Patient-specific restoration of constitutional alignment within predefined safety boundaries in primary TKA using modern 3D navigation is a promising strategy for personalized joint reconstruction using a standard knee arthroplasty implant. It combines precision and reproducibility with high patient satisfaction by respecting each patient’s constitutional alignment. Full article

Surgical navigation and augmented reality (AR) are widely used in neurosurgery, spinal surgery, and orthopedics. However, their use in minimally invasive abdominal and thoracic soft-tissue surgery is limited, as tracking deformable, mobile organs is challenging. Recent advances in AR may address these challenges to improve intraoperative navigation. This systematic review, registered in PROSPERO (2024) and based on PRISMA guidelines, analyzes literature from 2014 to 2024 about AR in minimally invasive abdominal and thoracic soft-tissue surgery. It identifies target organs, describes AR hardware and software, and evaluates accuracy levels, usability outcomes, clinical benefits, technical limitations, and research needs. Searches of PubMed, Web of Science, and Embase for English-language studies found 1297 records, of which only 28 (2%) met the inclusion criteria. Nearly half (n =12; 42%) focused on liver surgery; none on gynecologic surgery. The AR devices varied in tracking methods, image processing, visualization, and display. Overall, AR improved anatomical guidance and procedural planning, especially in complex surgeries. Integration with robotic systems may further boost visualization, precision, and workflow, though challenges remain in standardization, large-cohort validation, and workflow integration. Full article