Search Results (50)

Background: Over the years, surgical techniques have evolved, resulting in an abundance of available procedures in the armamentarium of metabolic and bariatric surgeons, and the technology has also advanced in a similar way. Significant steps have been made in stapling technology especially, introducing artificial intelligence (AI) in optimizing this technology for better treatment outcomes. The introduction of AI in stapling technology showed a decrease in potential stapling complications not only in MBS, but also in other (surgical) specialties. Areas Covered: This review will cover the general principles of stapling in surgery, but with an emphasis on both the technical and anatomical considerations. We will also discuss the mechanisms of staplers and potential safety hazards. Finally, we will focus on how AI is integrated in stapling technology, potential pros and cons, and areas for future development of stapling technology and the integration of AI. Conclusions: In metabolic and bariatric surgery, stapling is a technical procedure that requires a comprehensive understanding of the anatomical and physiological characteristics of the target tissue. Variability in tissue thickness, vascularity, elasticity, and mechanical load, compounded by patient-specific factors and intraoperative dynamics, demands constant vigilance and adaptability from the surgeon. The integration of AI and digital technologies offers potential improvements in refining this process. By providing real-time feedback on tissue properties and supporting intraoperative decision-making, these tools can assist surgeons in optimizing staple-line integrity and minimizing complications. The ongoing combination of surgical expertise with intelligent technology may contribute to advancing precision stapling in metabolic and bariatric surgery. Full article

Artificial intelligence (AI) enhances anesthesiology by introducing adaptive systems that improve clinical precision, safety, and responsiveness. This review examines the integration of AI in anesthetic practice, with a focus on closed-loop systems that exemplify autonomous control. These platforms integrate continuous physiologic inputs, such as BIS, EEG, heart rate, and blood pressure, to titrate anesthetic agents in real time, providing more consistent and responsive management than manual methods. Predictive algorithms reduce intraoperative hypotension by up to 40%, and systems such as McSleepy demonstrate greater accuracy in maintaining anesthetic depth and shortening recovery times. In critical care, AI supports sedation management, reduces clinician cognitive load, and standardizes care delivery during high-acuity procedures. The review also addresses the ethical, legal, and logistical challenges to widespread adoption of AI. Key concerns include algorithmic bias, explainability, and accountability for machine-generated decisions and disparities in access due to infrastructure demands. Regulatory frameworks, such as HIPAA and GDPR, are discussed in the context of securing patient data and ensuring its ethical deployment. Additionally, AI may play a transformative role in global health through remote anesthesia delivery and telemonitoring, helping address anesthesiologist shortages in resource-limited settings. Ultimately, AI-guided closed-loop systems do not replace clinicians; instead, they extend their capacity to deliver safe, responsive, and personalized anesthesia. These technologies signal a shift toward robotic anesthesia, where machine autonomy complements human oversight. Continued interdisciplinary development and rigorous clinical validation will determine how AI integrates into both operating rooms and intensive care units. Full article

Aims/Background: The growing integration of artificial intelligence (AI) into clinical medicine has opened new possibilities for enhancing diagnostic accuracy, therapeutic decision-making, and biomedical innovation across several domains. This review is aimed to evaluate the clinical applications of AI across five key domains of medicine: diagnostic imaging, clinical decision support systems (CDSS), surgery, pathology, and drug discovery, highlighting achievements, limitations, and future directions. Methods: A comprehensive PubMed search was performed without language or publication date restrictions, combining Medical Subject Headings (MeSH) and free-text keywords for AI with domain-specific terms. The search yielded 2047 records, of which 243 duplicates were removed, leaving 1804 unique studies. After screening titles and abstracts, 1482 records were excluded due to irrelevance, preclinical scope, or lack of patient-level outcomes. Full-text review of 322 articles led to the exclusion of 172 studies (no clinical validation or outcomes, n = 64; methodological studies, n = 43; preclinical and in vitro-only, n = 39; conference abstracts without peer-reviewed full text, n = 26). Ultimately, 150 studies met inclusion criteria and were analyzed qualitatively. Data extraction focused on study context, AI technique, dataset characteristics, comparator benchmarks, and reported outcomes, such as diagnostic accuracy, area under the curve (AUC), efficiency, and clinical improvements. Results: AI demonstrated strong performance in diagnostic imaging, achieving expert-level accuracy in tasks such as cancer detection (AUC up to 0.94). CDSS showed promise in predicting adverse events (sepsis, atrial fibrillation), though real-world outcome evidence was mixed. In surgery, AI enhanced intraoperative guidance and risk stratification. Pathology benefited from AI-assisted diagnosis and molecular inference from histology. AI also accelerated drug discovery through protein structure prediction and virtual screening. However, challenges included limited explainability, data bias, lack of prospective trials, and regulatory hurdles. Conclusions: AI is transforming clinical medicine, offering improved accuracy, efficiency, and discovery. Yet, its integration into routine care demands rigorous validation, ethical oversight, and human-AI collaboration. Continued interdisciplinary efforts will be essential to translate these innovations into safe and effective patient-centered care. Full article

Background: Hepato-pancreato-biliary (HPB) surgery involves operations that depend heavily on precise imaging, careful planning, and intraoperative decision-making. The rapid emergence of artificial intelligence (AI) and digital tools has assisted in these domains. Methods: We performed a PRISMA-guided systematic review (searches through June 2025) of AI/digital technologies applied to HPB surgical care, including novel models such as machine learning, deep learning, radiomics, augmented/mixed reality, and computer vision. Our focus was for eligible studies to address imaging interpretation, preoperative planning, intraoperative guidance, or outcome prediction. Results: In total, 38 studies met inclusion criteria. Imaging models constructed with AI showed high diagnostic performance for lesion detection and classification (commonly AUC ~0.80–0.98). Moreover, risk models using machine learning frequently exceeded traditional scores for predicting postoperative complications (e.g., pancreatic fistula). AI-assisted three-dimensional visual reconstructions enhanced anatomical understanding for preoperative planning, while augmented and mixed-reality systems enabled real-time intraoperative navigation in pilot series. Computer-vision systems recognized critical intraoperative landmarks (e.g., critical view of safety) and detected hazards such as bleeding in near real time. Most of the studies included were retrospective, single-center, or feasibility designs, with limited external validation. Conclusions: The usage of AI and digital tools show promising results across the HPB pathway—from preoperative diagnostics to intraoperative safety and guidance. The evidence to date supports technical feasibility and suggests clinical benefit, but routine adoption and further conclusions should await prospective, multicenter validation and consistent reporting. With continued refinement, multidisciplinary collaboration, appropriate cost effectiveness, and attention to ethics and implementation, these technologies could improve the precision, safety, and outcomes of HPB surgery. Full article

Three-dimensional (3D) modeling and printing technologies are increasingly used in pediatric surgery, offering improved anatomical visualization, surgical planning, and personalized approaches to complex conditions. Compared to standard imaging, patient-specific 3D models—virtual or printed—provide a more intuitive spatial understanding of congenital anomalies, tumors, and vascular anomalies. This review compiles evidence from pediatric surgical fields including oncology, abdominal, and thoracic surgery, highlighting the clinical relevance of 3D applications. The technological workflow—from image segmentation to computer-aided design (CAD) modeling and multimaterial printing—is described, emphasizing accuracy, reproducibility, and integration into hospital systems. Several clinical cases are presented: neuroblastoma, cloacal malformation, conjoined twins, and two cases of congenital diaphragmatic hernia (one with congenital pulmonary airway malformation, CPAM). In each, 3D modeling enhanced anatomical clarity, increased surgeon confidence, and supported safer intraoperative decision-making. Models also improved communication with families and enabled effective multidisciplinary planning. Despite these advantages, challenges remain, such as production time, cost variability, and lack of standardization. Future directions include artificial intelligence-based automation, expanded use of virtual and mixed reality, and prospective validation studies in pediatric cohorts. Overall, 3D modeling represents a significant advance in pediatric precision surgery, with growing evidence supporting its safety, clinical utility, and educational value. Full article

Purpose: This review aims to synthesize current knowledge of anatomical variations of the popliteofibular ligament (PFL) and evaluate the clinical relevance of the classification system proposed by Olewnik et al. in the context of the diagnosis, surgical treatment, and rehabilitation of posterolateral corner (PLC) injuries. Methods: A comprehensive analysis of anatomical, surgical, and radiological studies concerning the PFL was conducted. The implications of PFL morphological variants were examined across clinical applications, with an emphasis on reconstructive strategies, imaging interpretation, and rehabilitation planning. Emerging research directions, including AI-supported imaging and personalized algorithms, were also explored. Results: Olewnik’s classification identifies three distinct types of PFL, each with unique structural and biomechanical properties. Recognizing these variants enhances intraoperative orientation, facilitates tailored surgical techniques, and supports individualized rehabilitation protocols. Variant-specific biomechanics, identified via cadaveric studies and imaging, are essential for optimizing functional outcomes and minimizing postoperative instability. Furthermore, the classification offers a platform for developing future diagnostic and decision-support tools using artificial intelligence. Conclusions: The Olewnik et al. classification system should be adopted as a modern anatomical standard for the PFL. Its integration into clinical practice has the potential to improve surgical precision, reduce complication rates, and enhance patient-specific treatment planning. This framework also supports future advancements in orthopedic imaging, education, and AI-driven diagnostics. Beyond descriptive anatomy, we provide a pragmatic surgical algorithm for PLC repair/reconstruction that accounts for scar- and fibrosis-dominated fields and the limited bone stock of the fibular head. Full article

Background: Timely and accurate identification of wound infections is essential for effective management, yet remains clinically challenging. This study evaluated the utility of a near-infrared autofluorescence imaging system (Fluobeam^®, Fluoptics, Grenoble, France) and a thermal imaging system (FLIR^®, Teledyne LLC, Thousand Oaks, CA, USA) for detecting bacterial and fungal infections in chronic wounds. Fluobeam^® enables real-time visualization of microbial autofluorescence without exogenous contrast agents, whereas FLIR^® detects localized thermal changes associated with infection-related inflammation. Methods: This retrospective clinical study included 33 patients with suspected wound infections. All patients underwent autofluorescence imaging using Fluobeam^® and concurrent thermal imaging with FLIR^®. Imaging findings were compared with microbiological culture results, clinical signs of infection, and semi-quantitative microbial burdens. Results: Fluobeam^® achieved a sensitivity of 78.3% and specificity of 80.0% in detecting culture-positive infections. Fluorescence signal intensity correlated strongly with microbial burden (r = 0.76, p < 0.01) and clinical indicators, such as exudate, swelling, and malodor. Pathogens with high metabolic fluorescence, including Pseudomonas aeruginosa and Candida spp., were consistently identified. Representative cases demonstrate the utility of fluorescence imaging in guiding targeted debridement and enhancing intraoperative decision-making. Conclusions: Near-infrared autofluorescence imaging with Fluobeam^® and thermal imaging with FLIR^® offer complementary, noninvasive diagnostic insights into microbial burden and host inflammatory response. The combined use of these modalities may improve infection detection, support clinical decision-making, and enhance wound care outcomes. Full article

Background/Objectives: This systematic review aims to synthesize recent studies on the integration of artificial intelligence (AI) into robotic surgery for oncological patients. It focuses on studies using real patient data and AI tools in robotic oncologic surgery. Methods: This systematic review followed PRISMA guidelines to ensure a robust methodology. A comprehensive search was conducted in June 2025 across Embase, Medline, Web of Science, medRxiv, Google Scholar, and IEEE databases, using MeSH terms, relevant keywords, and Boolean logic. Eligible studies were original research articles published in English between 2024 and 2025, focusing on AI applications in robotic cancer surgery using real patient data. Studies were excluded if they were non-peer-reviewed, used synthetic/preclinical data, addressed non-oncologic indications, or explored non-robotic AI applications. This approach ensured the selection of studies with practical clinical relevance. Results: The search identified 989 articles, with 17 duplicates removed. After screening, 921 were excluded, and 37 others were eliminated for reasons such as misalignment with inclusion criteria or lack of full text. Ultimately, 14 articles were included, with 8 using a retrospective design and 6 based on prospective data. These included articles that varied significantly in terms of the number of participants, ranging from several dozen to several thousand. These studies explored the application of AI across various stages of robotic oncologic surgery, including preoperative planning, intraoperative support, and postoperative predictions. The quality of 11 included studies was very good and good. Conclusions: AI significantly supports robotic oncologic surgery at various stages. In preoperative planning, it helps estimate the risk of conversion from minimally invasive to open colectomy in colon cancer. During surgery, AI enables precise tumor and vascular structure localization, enhancing resection accuracy, preserving healthy tissue, and reducing warm ischemia time. Postoperatively, AI’s flexibility in predicting functional and oncological outcomes through context-specific models demonstrates its value in improving patient care. Due to the relatively small number of cases analyzed, further analysis of the issues presented in this review is necessary. Full article

Artificial intelligence has shown great promise in advancing intelligent surgical systems. Among its applications, surgical video action recognition plays a critical role in enabling accurate intraoperative understanding and decision support. However, the task remains challenging due to the temporal continuity of surgical scenes and the long-tailed, semantically entangled distribution of action triplets composed of instruments, verbs, and targets. To address these issues, we propose TriQuery, a query-based model for surgical triplet recognition and classification. Built on a multi-task Transformer framework, TriQuery decomposes the complex triplet task into three semantically aligned subtasks using task-specific query tokens, which are processed through specialized attention mechanisms. We introduce a Multi-Query Decoding Head (MQ-DH) to jointly model structured subtasks and a Top-K Guided Query Update (TKQ) module to incorporate inter-frame temporal cues. Experiments on the CholecT45 dataset demonstrate that TriQuery achieves improved overall performance over existing baselines across multiple classification tasks. Attention visualizations further show that task queries consistently attend to semantically relevant spatial regions, enhancing model interpretability. These results highlight the effectiveness of TriQuery for advancing surgical video understanding in clinical environments. Full article

Introduction: Near-infrared fluorescence (NIRF) imaging with indocyanine green (ICG) is now widely regarded as a valuable aid in decision-making for complex hepatobiliary procedures, with increasing support from recent studies. Methods: We performed a systematic review following PRISMA guidelines, utilizing PubMed, CINAHL, and EMBASE databases to locate studies on the perioperative use ICG in pediatric hepatobiliary surgeries. Two independent reviewers assessed all articles for eligibility based on predefined inclusion criteria. We collected data on study design, patient demographics, surgical indications, ICG dosing, timing of ICG injection, and perioperative outcomes. Results: Forty-three articles, including 930 pediatric patients, from 1989 to 2025 met the inclusion criteria for narrative synthesis in our systematic review, of which 22/43 (51.2%) were retrospective studies, 15/43 were case reports (34.9%), 3/43 (7.0%) were experimental studies, and the other three were prospective comparative studies (7.0%). The current clinical applications of ICG in hepatobiliary pediatric surgery include bile duct surgery (cholecystectomy, choledochal cyst, biliary atresia), reported in 17 articles (39.5%), liver tumor resection, reported in 15 articles (34.9%), liver transplantation, reported in 6 articles (14.6%), and liver function determination, reported in 5 articles (12.2%). Conclusions: ICG fluorescence navigation in pediatric hepatobiliary surgery is a highly promising and safe technology that allows for the intraoperative localization of anatomic biliary structures, aids in the identification and resection of liver tumors, and can accurately determine hepatic function. The lack of comparative and prospective studies, and the variability of the dose and timing of administration are the main limitations. Full article

Oncoplastic breast-conserving surgery (OBCS), or oncoplastic surgery, has revolutionized the surgical management of breast cancer by integrating oncologic principles with reconstructive techniques to optimize both cancer control and aesthetic outcomes following breast-conserving surgery (BCS). Since its inception in the 1980s, the field has evolved significantly, incorporating a range of volume displacement and volume replacement strategies to restore breast contour after partial mastectomy. This review explores the current practices and key surgical considerations of OBCS. It highlights the role of preoperative multidisciplinary planning, patient selection, anatomical and vascular knowledge, and intraoperative technique in optimizing results. Barriers to access—including disparities in training, insurance, and geographic availability—are addressed, alongside efforts by professional societies like the American Society of Breast Surgeons (ASBS) to standardize definitions and practices. The review also outlines strategies for minimizing complications and enhancing oncologic, reconstructive, and patient-reported outcomes. By offering a comprehensive framework for clinical decision-making, this paper aims to support broader adoption and refinement of OBCS as a standard component of breast cancer care. Full article

Real-time 3D reconstruction in minimally invasive surgery improves depth perception and supports intraoperative decision-making and navigation. However, endoscopic imaging presents significant challenges, such as specular reflections, low-texture surfaces, and tissue deformation. We present a novel, deterministic and iterative stereo-matching method based on adaptive support weights that is tailored to these constraints. The algorithm is implemented in CUDA and C++ to enable real-time performance. We evaluated our method on the Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED) dataset and a custom synthetic dataset using the mean absolute error (MAE), root mean square error (RMSE), and frame rate as metrics. On SCARED datasets 8 and 9, our method achieves MAEs of 3.79 mm and 3.61 mm, achieving 24.9 FPS on a system with an AMD Ryzen 9 5950X and NVIDIA RTX 3090. To the best of our knowledge, these results are on par with or surpass existing deterministic stereo-matching approaches. On synthetic data, which eliminates real-world imaging errors, the method achieves an MAE of 140.06 μm and an RMSE of 251.9 μm, highlighting its performance ceiling under noise-free, idealized conditions. Our method focuses on single-shot 3D reconstruction as a basis for stereo frame stitching and full-scene modeling. It provides accurate, deterministic, real-time depth estimation under clinically relevant conditions and has the potential to be integrated into surgical navigation, robotic assistance, and augmented reality workflows. Full article

Background: Microsurgery is a highly complex and technically demanding field within reconstructive surgery, with outcomes heavily dependent on meticulous planning, precision, and postoperative monitoring. Over the last five years, artificial intelligence (AI) has emerged as a transformative tool across all phases of microsurgical care, offering new capabilities in imaging analysis, intraoperative decision support, and outcome prediction. Methods: A comprehensive narrative review was conducted to evaluate the peer-reviewed literature published between 2020 and May 2025. Multiple databases, including PubMed, Embase, Cochrane, Scopus, and Web of Science, were searched using combinations of controlled vocabulary and free-text terms relating to AI and microsurgery. Studies were included if they described AI applications during the preoperative, intraoperative, or postoperative phases of microsurgical care in human subjects. Discussion: Using predictive models, AI demonstrated significant utility in preoperative planning through automated perforator mapping, flap design, and individualised risk stratification. AI-enhanced augmented reality and perfusion analysis tools improved precision intraoperatively, while innovative robotic platforms and intraoperative advisors showed early promise. Postoperatively, mobile-based deep learning applications enabled continuous flap monitoring with sensitivities exceeding 90%, and AI models accurately predicted surgical site infections, transfusion needs, and long-term outcomes. Despite these advances, most studies relied on retrospective single-centre data, and large-scale, prospective validation remains limited. Conclusions: AI is poised to enhance microsurgical precision, safety, and efficiency. However, its integration is challenged by data heterogeneity, generalisability concerns, and the need for human oversight in nuanced clinical scenarios. Standardised data collection and multicentre collaboration are vital for robust, equitable AI deployment. With careful validation and implementation, AI holds the potential to redefine microsurgical workflows and improve patient outcomes across diverse clinical settings. Full article

Background/Objectives: Immediate implant-based breast reconstruction is an established option for selected patients undergoing mastectomy. However, patients with large and ptotic breasts present specific reconstructive challenges, often requiring tailored approaches to minimize complications and optimize aesthetics. This pilot study aimed to evaluate the clinical feasibility and effectiveness of a mobile application developed to support intraoperative decision-making based on an institutional algorithm for breast reconstruction. It is also important to underline that this pilot study was exploratory in nature and primarily aimed at assessing feasibility and adherence to an app-based decision pathway, rather than comparative efficacy. Methods: We conducted a prospective observational study from October 2023 to December 2024 at the University Hospital of Sassari. Female patients with large and ptotic breasts undergoing immediate implant-based reconstruction were included. A mobile app, developed using MIT App Inventor 2, implemented our institution’s algorithm and guided surgeons through both preoperative and intraoperative decision-making. Surgical options included subpectoral, prepectoral with autologous fascial flaps, or prepectoral with acellular dermal matrix (ADM) reconstruction, depending on flap thickness and fascia integrity. Results: Sixteen patients (21 reconstructed breasts) were included. Surgical planning and execution followed app-generated recommendations in all cases, with no intraoperative deviations. Subpectoral reconstruction was performed in six patients, prepectoral with ADM in eight, and prepectoral with fascial flaps in two. The app was rated positively by all surgeons and facilitated consistent decision-making. Conclusions: The proposed mobile application, described in this pilot study, proved to be a feasible and effective decision-support tool for implant-based breast reconstruction in patients with challenging anatomy. It standardized surgical choices, supported training, and has the potential to enhance reproducibility and safety in complex reconstructive procedures. Full article

Background/Objectives: Total hip arthroplasty (THA) remains a widely utilized and effective intervention for patients with end-stage hip osteoarthritis. Although multiple surgical approaches and fixation techniques are available, their application in non-tertiary clinical settings is less frequently documented. This study primarily aims to provide an educational overview of stepwise THA procedures using intraoperative visual documentation, with a secondary, exploratory assessment of postoperative outcomes related to surgical approach and fixation strategy. Methods: A prospective observational study was conducted at Arad Clinical Emergency County Hospital between March 2023 and March 2024, involving 23 patients undergoing primary THA. Patients received either cemented or uncemented femoral components based on intraoperative bone quality. Procedures were documented using stepwise intraoperative photographs and postoperative radiographs. Recovery was assessed using the Harris Hip Score (HHS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at both six weeks and six months postoperatively. Results: Both lateral (Hardinge) and posterolateral approaches provided adequate exposure with reproducible results. Cemented implants allowed for immediate full weight-bearing and were preferred in elderly patients with poor bone quality, while uncemented components were used in younger patients with good bone density, requiring a delayed weight-bearing protocol. Functional scores improved in both groups between six weeks and six months. At six weeks, the mean HHS was 87.6 ± 6.2 and WOMAC 18.3 ± 4.8; by six months, these improved to 91.8 ± 5.1 and 12.7 ± 3.9, respectively. Cemented fixation demonstrated slightly better outcomes at both time points; however, intergroup differences remained below the Minimal Clinically Important Difference (MCID) thresholds. Conclusions: Tailored surgical approaches and fixation strategies, guided by intraoperative assessment, result in favorable short- and mid-term recovery profiles in THA. The integration of intraoperative visual documentation and patient-reported outcome measures (PROMs) enhances procedural transparency while supporting evidence-based decision-making and surgical training. Full article