Search Results (629)

Background/Objectives: Vegetation length is a guideline-endorsed criterion for surgery in left-sided infective endocarditis (LSIE). However, its measurement is highly variable with crucial implications for decision making. A standardized measurement system would not only facilitate decision making in these patients, but also cutoff-point optimization for improving clinical guidelines. For this purpose, this work introduces an Artificial Intelligence (AI)-based system capable of extracting vegetation length from standard transesophageal echocardiography (TEE). Methods: Five echocardiographers independently measured the vegetation length of 76 vegetations from 67 consecutive patients with LSIE using offline TEE images. The AI-based system was trained on a multicenter registry with 353 patients with LSIE, comprising 282,096 echocardiographic frames annotated by an independent expert. This system was applied to measure vegetation length as an independent observer. The variability and correlation between operators and the system were assessed. Results: Lin’s concordance correlation coefficient between the AI-based system and the mean of the measurements obtained by the echocardiographers was 0.74, comparable to the coefficient obtained between the echocardiographers themselves. Bland–Altman analysis showed a mean difference of −1.0 mm between the AI-based system and the mean of the measurements obtained by the echocardiographers. Conclusions: The AI-based system for vegetation measurement demonstrates a high level of correlation and agreement with experts, similar to the concordance between human operators themselves. This level of agreement suggests that the method has the potential to reduce inter-operator variability in the measurement process. Whether AI-based vegetation size improves embolism prediction has to be further investigated. Full article

Background/Objectives: In modern medicine, humanization of care is a central theme that highlights the complementarity of clinical care with empathy, communication, and patient-centered values. The increasing use of robotic surgery is rapidly modifying surgical practice, introducing new challenges and opportunities in preserving the human dimension of care. This review aimed to synthesize the available evidence and propose a conceptual model of humanized robotic surgical care. Methods: A structured narrative review of the PubMed/MEDLINE database (from inception to January 2026) was conducted using predefined keywords related to humanization of care, robotic surgery, patient perception, surgeon experience, human factors, communication, ethics, and technological mediation. Relevant English-language publications were critically synthesized to develop a conceptual framework. Results: The literature indicates that robotic surgery influences humanized care through three interconnected domains. First, patients frequently perceive robotic surgery as more precise and technologically advanced, which may generate unrealistic expectations and misconceptions regarding robotic autonomy. Second, robotic platforms reshape the surgeon’s experience by improving ergonomics while simultaneously modifying cognitive workload, sensory feedback, and professional identity. Third, technology itself acts as an active mediator influencing communication, trust, decision-making, and relational dynamics. Building upon these findings, we propose an original triangular conceptual framework integrating the patient, the surgeon, and the technology as three interdependent determinants of humanized robotic surgical care. The framework also provides a conceptual basis for understanding the future integration of artificial intelligence into surgical practice. Conclusions: Humanization of care in the era of robotic surgery requires an integrated approach that recognizes the interdependence of patient perception, surgeon experience, and technological mediation. Ensuring effective communication, supporting surgeon well-being, and preserving ethical principles will be essential to aligning innovation with patient-centered care. Full article

Background/Objectives: Orthognathic surgery is performed to improve facial esthetics and function in patients with dentofacial deformities. This study aimed to evaluate the impact of orthognathic surgery on perceived facial age using an artificial intelligence (AI)-based age-estimation tool and to examine accompanying changes in self-rated flourishing and social appearance anxiety. Methods: This retrospective, single-arm, before-and-after study included 27 patients who underwent bilateral sagittal split ramus osteotomy (BSSRO) and/or Le Fort I osteotomy. Preoperative and postoperative standardized frontal photographs were analyzed with the AI-based application “AgeBot: How Old Do I Look?”. Participants also completed the Social Appearance Anxiety Scale (SAAS) and the Flourishing Scale (FS; Turkish adaptation by Telef). Both questionnaires were completed at a single postoperative interview—first for the remembered preoperative state and then for the current postoperative state—so the psychosocial pre–post comparisons reflect retrospectively perceived change. Normality was assessed on the paired differences; the primary AI-age outcome was analyzed with the paired t-test (confirmed by the Wilcoxon signed-rank test) and the psychosocial outcomes with the Wilcoxon signed-rank test, with effect sizes reported. A data-informed sensitivity analysis, rather than an a priori power calculation, accompanied the primary outcome. Results: AI-estimated facial age did not change significantly after surgery (mean difference [preoperative−postoperative] 0.11 ± 2.49 years, 95% CI −0.87 to 1.09, p = 0.818; Cohen’s dz = 0.04), and the confidence interval excluded the previously reported 1.31-year reduction. Exploratory change-score comparisons showed no significant differences by sex or smoking status. Postoperative SAAS scores were lower than retrospective preoperative scores (median 37 [IQR 24.5–42.0] vs. 51 [43.0–56.0]; Z = −4.07, p < 0.001, r = 0.55), and postoperative FS scores were higher (median 46 [40.0–51.5] vs. 45 [38.5–45.0]; Z = −2.45, p = 0.014, r = 0.37). Conclusions: Orthognathic surgery was not associated with a statistically significant change in AI-estimated facial age in this small cohort. More favorable social appearance anxiety and flourishing scores were observed following treatment, but the retrospective, uncontrolled assessment precludes causal attribution. AI-assisted facial age estimation may complement patient-reported outcomes, but validated, repeatability-tested tools are required before it can serve as a primary outcome measure. Full article

Background/Objectives: Cleft lip and palate are among the most common congenital craniofacial anomalies and require careful postoperative care after surgical repair. Mothers, as primary caregivers, are expected to manage feeding, wound care, oral hygiene, and the early recognition of complications; however, gaps in postoperative care knowledge may limit safe home care. This pilot study aimed to evaluate the effect of an artificial intelligence (AI)-supported video-based educational intervention on mothers’ knowledge of postoperative care after cleft lip and/or palate surgery. Methods: This single-group quasi-experimental pre–post pilot study was conducted between April and December 2025 in the Plastic, Reconstructive, and Aesthetic Surgery Clinic of a tertiary hospital in Istanbul. Thirty mothers of children aged 0–18 years who underwent cleft lip and/or palate surgery were included. Data were collected using a Demographic Information Form and a Nutrition and Care Knowledge Questionnaire. Mothers completed the questionnaire before and immediately after watching a standardized AI-supported educational video developed by the researchers. Pre- and post-intervention knowledge scores were compared using nonparametric statistical tests. Results: A total of 30 mothers participated in the study. Child-related sociodemographic and clinical variables were recorded to describe the children’s profile. Among the children, 56.7% were female, and 56.7% were aged 0–3 years. Post-intervention knowledge scores increased in the total score and in all subdomains, including postoperative care, feeding, and complication monitoring. The greatest improvement was observed in complication monitoring, which had the lowest baseline scores. Knowledge gains were observed across participant subgroups. Conclusions: This pilot study suggests that AI-supported video-based education may improve mothers’ short-term knowledge of postoperative care after cleft lip and/or palate surgery. However, because of the single-group design, small sample size, and immediate post-test assessment, the findings should be interpreted as preliminary. Larger controlled studies with longer follow-up are needed to examine knowledge retention and the potential effects of this approach on caregiving practices. Full article

Background: Basal cell carcinoma (BCC) of the skin is the most common cancer in humans, and its incidence rises annually. Despite its low death rate, BCC causes significant morbidity because of its destructive nature to local tissues. The aim of this study was to review the state of the BCC research landscape using data published in Scopus from 1972 to 2023. Methods: Using the R package Bibliometrix, we first determined authors, countries, journals, and main topics behind the advancement of BCC research. The Latent Dirichlet Allocation (LDA), a probabilistic topic algorithm, was applied to automatically discover latent (hidden) thematic structures. Additionally, the HJ-Biplot was also chosen to visualize graphical representations of multivariate scientometric and bibliometric data to improve the LDA outcome. Results: Over five decades, we discovered 32 unique themes. BCC research has shifted from studying tumor and immunohistochemically characterization, epidemiology, and surgical/histological clearance to the advancement of diagnostic and imaging techniques, like dermoscopy and reflectance confocal microscopy (RCM). Another exciting BCC research trend has been the discovery of aberrant activation within the Hedgehog signaling. Finally, patient treatment, especially surgery, radiotherapy, topical fluorouracil, and imiquimod, is an object of intense research. Conclusions: By displaying each topic as a group of related words, this in-depth exploration outlined emerging avenues in BCC evidence-based research that will benefit from the development of cutting-edge diagnostic procedures, as well as a deeper knowledge of BCC etiology and genetic underpinnings, the quality of life in BCC patients after surgery, and the application of artificial intelligence. Full article

Objectives: The facial nerve (FN) possesses one of the most intricate anatomical courses in the head and neck, traversing the brainstem, temporal bone, and parotid gland before terminating within the muscles of facial expression. Owing to its complex anatomy, marked anatomical variability, and frequent distortion by adjacent pathology, preservation of FN integrity remains a fundamental challenge in skull base, otologic, and head and neck surgery. This review aims to provide a comprehensive synthesis of the contemporary literature regarding the clinical anatomy of the FN and to examine how anatomical variation, pathology-induced distortion, surgical strategy, and emerging technologies influence nerve preservation and functional outcomes. Methods: A comprehensive narrative review of the literature was conducted using PubMed, Scopus, and Google Scholar. Publications from 1983 through 2026 were searched using combinations of keywords, including “facial nerve,” “facial nerve anatomy,” “anatomical variation,” “vestibular schwannoma,” “hemifacial spasm,” “parotid surgery,” “facial nerve injury,” “facial nerve reconstruction,” “facial reanimation,” “diffusion tensor imaging,” “intraoperative neurophysiological monitoring,” and “artificial intelligence.” Peer-reviewed anatomical, radiological, clinical, and review articles published in English were included, while conference abstracts and studies lacking direct anatomical or surgical relevance were excluded. Particular emphasis was placed on surgically relevant anatomical variations, pathology-related anatomical distortion, advanced imaging modalities, intraoperative neurophysiological monitoring, reconstructive techniques, and predictors of postoperative facial nerve function. Results: Facial nerve preservation was found to depend on the interplay between individual anatomical variability, disease-related anatomical distortion, and operative strategy. In vestibular schwannoma surgery, nerve displacement, capsular adhesion, and cystic tumor degeneration were consistently associated with increased surgical complexity and less favorable postoperative facial function. In hemifacial spasm, successful microvascular decompression relied on precise identification of neurovascular conflict at the root exit zone. Within the parotid gland, substantial variability in branching architecture and surgical landmarks contributed to an increased risk of iatrogenic injury. Advanced imaging techniques, particularly diffusion tensor imaging tractography, improved preoperative prediction of FN location, while intraoperative neurophysiological monitoring enabled real-time assessment of neural integrity and functional preservation. Emerging artificial intelligence-based predictive models demonstrated potential to enhance patient-specific surgical planning and prognostication. Conclusions: Contemporary facial nerve surgery has evolved toward an individualized, anatomy-driven, and function-preserving paradigm supported by advanced imaging, intraoperative monitoring, and reconstructive strategies. Detailed understanding of both normal FN anatomy and pathology-induced anatomical distortion remains essential for optimizing surgical decision-making, maximizing nerve preservation, and improving long-term functional outcomes. Future developments integrating multimodal imaging, predictive analytics, and artificial intelligence may further refine patient-specific management and enhance postoperative facial function. Full article

Background/Objectives: Intraoperative ultrasound (IOUS) has become an integral component of modern hepatic oncology surgery, providing real-time imaging guidance during liver resections for hepatocellular carcinoma, colorectal liver metastases, and other primary or secondary hepatic malignancies. Despite substantial improvements in preoperative imaging modalities, occult lesions, disappearing metastases after chemotherapy, and complex vascular relationships continue to represent major intraoperative challenges. This structured narrative review aimed to evaluate the contemporary role of IOUS in hepatic oncology surgery, with particular emphasis on contrast-enhanced intraoperative ultrasound (CE-IOUS), minimally invasive liver surgery, navigation-assisted hepatectomy, and emerging artificial intelligence-based technologies. Methods: A structured literature review was conducted using PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases. Peer-reviewed studies, international guidelines, consensus statements, systematic reviews, and technological reports addressing IOUS applications in liver surgery were analyzed. Particular focus was placed on studies evaluating lesion detection, intraoperative strategy modification, disappearing colorectal liver metastases, parenchymal-sparing hepatectomy, laparoscopic and robotic liver surgery, navigation systems, augmented reality integration, and AI-assisted imaging technologies. Results: Contemporary evidence demonstrates that IOUS continues to significantly influence intraoperative decision-making despite advances in magnetic resonance imaging and multidetector computed tomography. CE-IOUS improves the detection of occult hepatic lesions and residual disease after systemic chemotherapy, particularly in disappearing colorectal liver metastases. IOUS-guided anatomical and parenchymal-sparing resections contribute to the preservation of functional liver parenchyma while maintaining oncologic radicality. In minimally invasive liver surgery, laparoscopic ultrasound remains essential for lesion localization and vascular mapping. Recent developments integrating navigation systems, augmented reality platforms, and AI-assisted image recognition suggest a progressive transition toward digitally integrated precision liver surgery. Conclusions: IOUS remains a cornerstone of modern hepatic oncology surgery and continues to evolve from a localization tool into a comprehensive platform for precision-guided liver resection. The integration of CE-IOUS, navigation technologies, and artificial intelligence may further enhance intraoperative accuracy, oncologic safety, and individualized surgical planning in the future. Full article

Pain and stress are frequent and clinically relevant challenges in neonatal intensive care, particularly among preterm and critically ill newborns exposed to repeated invasive procedures, mechanical ventilation, surgery, and advanced life-support interventions. Effective analgosedation is essential to reduce discomfort, attenuate physiological instability, improve tolerance of intensive care procedures, and potentially limit adverse neurodevelopmental consequences. However, neonatal pain and analgosedation management remain complex because of developmental immaturity, pharmacokinetic and pharmacodynamic variability, and the need to balance adequate analgosedation against treatment-related complications. This narrative review summarizes current evidence on analgosedation in the Neonatal Intensive Care Unit, focusing on clinical indications, pharmacological agents, non-pharmacological strategies, monitoring tools, adverse effects, and future perspectives. Opioids, benzodiazepines, dexmedetomidine, and ketamine each have specific potential benefits and limitations, requiring individualized selection, careful titration, and continuous reassessment. Non-pharmacological interventions, including oral sucrose, non-nutritive sucking, facilitated tucking, breastfeeding, skin-to-skin care, and environmental modulation, should be integrated into multimodal pain-management protocols. Validated instruments such as COMFORTneo, N-PASS, and PIPP-R support standardized assessment and guide therapeutic decisions. Future advances may derive from objective monitoring technologies, artificial intelligence, developmental pharmacology, and precision-medicine approaches. A multidisciplinary, protocol-driven, and family-centered strategy is essential to optimize neonatal comfort while minimizing avoidable drug exposure. Full article

Background/Objectives: Mitral valve surgery is associated with substantial perioperative heterogeneity and risk of postoperative complications. Although established risk scores such as EuroSCORE II provide population-level prognostic estimates, their performance may be limited in specific surgical populations and institutional settings. This pilot study aimed to develop and internally validate an interpretable center-specific machine learning model for perioperative risk stratification following mitral valve surgery and to explore its translational implementation through a prototype clinical application. Methods: A retrospective single-center study was conducted including 211 consecutive patients undergoing mitral valve surgery with ring implantation. Routinely available demographic, laboratory, and perioperative variables were evaluated as candidate predictors. The primary endpoint was a composite of major postoperative complications, including in-hospital mortality, stroke, conversion to sternotomy, and rethoracotomy. Predictive approaches included logistic regression, LASSO regression, and random forest classification. Internal validation was performed using 5-fold cross-validation and bootstrap resampling. Model explainability was assessed using regression coefficients and SHAP (SHapley Additive exPlanations) analysis. Results: The composite endpoint occurred in 34 patients (16.1%). In the complete-case final logistic regression model, apparent discrimination reached an AUC of 0.750 (95% CI 0.643–0.858), with a Brier score of 0.105. In the predefined train-test evaluation, the simplified logistic regression model achieved a test-set AUC of 0.67, while 5-fold cross-validation yielded a mean AUC of 0.75. LASSO regression achieved the highest cross-validated AUC (0.78), although with marked discrepancy between test-set and cross-validation performance, suggesting model instability. Across models, higher age, serum creatinine concentration, cardiopulmonary bypass duration, and cross-clamp time were associated with increased complication risk, whereas higher hemoglobin levels were associated with lower risk. Conclusions: This pilot study demonstrates the feasibility of developing interpretable center-specific machine learning models for perioperative risk stratification following mitral valve surgery. Simplified regression-based approaches provided clinically transparent predictions with moderate discriminatory performance, while penalized models showed potential for improved generalizability. Further multicenter validation is required before clinical implementation. Full article

The rapid advancement of robot-assisted minimally invasive surgery (RAMIS) has created an increasing demand for integrated solutions that combine advanced robotic actuation, sensing, and intelligent control within unified training and operational frameworks. This paper presents a Digital Twin–based virtual reality (VR) interaction and control system developed for an innovative parallel surgical robot, designed to support both surgical training and real-time robot interaction. The proposed framework extends a conventional VR simulator into a bidirectional Digital Twin architecture, enabling real-time synchronization between a virtual environment and the physical robotic system. The system integrates the ATHENA parallel robot, characterized by a 4-degree-of-freedom architecture with a Remote Center of Motion (RCM) constraint, together with a flexible laparoscopic instrument providing enhanced dexterity. Interaction is achieved using VR controllers, allowing intuitive manipulation of the robotic system within an immersive environment. To enhance operational performance, an artificial intelligence module based on neural networks is integrated as an assistive component, providing real-time trajectory refinement and motion guidance. The trained model is deployed using an ONNX-compatible runtime, ensuring efficient inference and seamless integration within the control architecture. The proposed system is validated through experimental evaluation of user interaction and task execution performance, as well as through external motion assessment using an OptiTrack optical tracking system. The results demonstrate improvements in motion stability, execution efficiency, and user interaction quality, while maintaining a high level of control intuitiveness. The findings highlight the potential of Digital Twin–based VR systems as a unifying platform for surgical training, interaction, and intelligent assistance in next-generation medical robotic systems. Full article

Postoperative wound healing complications present a major challenge in plastic and reconstructive surgery, prolonging recovery and impairing outcomes. Early risk identification is difficult due to complex interactions among clinical, laboratory, and molecular factors. This study developed and evaluated machine-learning (ML) models to predict wound healing outcomes and identify key complication predictors. Utilizing a dataset of 95 women and 76 variables (including hematological, biochemical, coagulation, and gene expression profiles), we evaluated several ML approaches, including Decision Tree, Extra Trees, Gaussian/Bernoulli Naive Bayes, Logistic Regression, and Support Vector Machine. Model performance was assessed via k-fold cross-validation, ROC analysis, and SHAP feature importance. Molecular markers (COL1A1, MMP9, MAPK1, MAPK8, IL10, and CCL2) emerged as the strongest predictors, whereas conventional clinical variables showed limited value. The models achieved high discriminative performance, with validation ROC–AUC values ranging from 0.903 to 0.913. Extra Trees and Gaussian Naive Bayes demonstrated the highest sensitivity for detecting complications (Recall = 0.820 ± 0.238 and 0.807 ± 0.246, respectively). These findings highlight the value of integrating molecular-genetic biomarkers with ML for personalized risk stratification and preventive care in reconstructive surgery. Full article

Sleep-related breathing disorders (SRBDs) encompasses a spectrum of conditions that disrupt ventilation during sleep, leading to fragmented sleep and impaired gas exchange. Their high prevalence and substantial neurocognitive and mental health outcomes make SRBD clinically significant across multiple medical disciplines. Traditional management includes lifestyle modifications and positive airway pressure (PAP). When non-surgical measures fail or anatomical factors predominate, a range of surgical approaches may be employed, such as uvulopalatopharyngoplasty (UPPP) or maxillomandibular advancement (MMA). There are many notable emerging surgical advancements, such as hypoglossal nerve stimulation (HNS), transoral robotic surgery (TORS), and minimally invasive radiofrequency technologies (RFA), that have offered improved outcomes for select patients. Advances in diagnostic tools, such as portable home sleep technologies and drug-induced sleep endoscopy (DISE), further support precision-based care. Collectively, the expanding range of therapeutic and diagnostic innovations is enabling clinicians to deliver individualized care and improve long-term outcomes for patients with SRBD. Full article

Intracerebral hemorrhage (ICH) remains one of the most severe forms of stroke and is associated with high mortality, poor functional outcomes, and substantial healthcare burden worldwide. Despite advances in neurocritical care and minimally invasive surgical techniques, the management of ICH remains challenging because of disease heterogeneity, rapid neurological deterioration, and the lack of effective individualized treatment strategies. In recent years, artificial intelligence (AI) has emerged as a promising tool for improving the diagnosis, prognostic evaluation, and precision management of ICH. A systematic literature search was performed in PubMed, Web of Science, and Embase to identify studies on AI applications in ICH, with predefined inclusion criteria focusing on imaging analysis, prognostic prediction, clinical decision support, and minimally invasive surgery. This review summarizes the major clinical applications, limitations, and future directions of AI in ICH, including multimodal foundation models, intelligent surgical assistance, and personalized precision care. Recent studies have demonstrated that AI-based models can significantly improve the accuracy of hematoma segmentation, hematoma expansion prediction, and functional outcome prognostication compared with conventional approaches. Despite encouraging progress, several important barriers continue to limit clinical translation, including data heterogeneity, limited external validation, insufficient interpretability, ethical and regulatory concerns, and challenges in workflow integration. Overall, AI has the potential to transform ICH management from conventional experience-based practice to data-driven, personalized, and precision neurosurgical care. Full article

Imaging techniques used for the care of cutaneous melanoma patients have greatly changed over the past century, from symptom-driven radiography toward a multimodality framework integrated for staging, directing surgery, and systemic therapy, and surveillance. Historically, clinical evaluation and skin exams have been the tenets of melanoma diagnosis and staging. In recent years, noninvasive imaging, such as dermoscopy, total-body photography and reflectance confocal microscopy, has expanded the diagnostic toolset for primary melanoma detection. Concurrently, several imaging techniques have been developed to detect metastases and follow disease progression, including computed tomography (CT), magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT), lymphoscintigraphy, and single-photon emission computed tomography/computed tomography (SPECT/CT). The use of immune checkpoint inhibitors has also altered imaging interpretation by introducing atypical response patterns, including pseudoprogression, requiring immune-adapted assessment frameworks such as Immune Response Evaluation Criteria in Solid Tumors (iRECIST). While there is a strong consensus for high-risk patients, imaging techniques and surveillance schedules for low-risk patients (stage I/II) remain controversial due to limited supporting evidence and conflicting data on costs and patient benefit. The development of new technologies, including image-guided surgery, non-FDG PET tracers, phone apps, artificial intelligence-assisted image analysis, and radiomics, may further change melanoma imaging. The aim of this review is to detail the historical evolution of melanoma imaging, the development of new imaging techniques, and their role and future in clinical practice. Full article

Radioguided surgery has become a key component of contemporary breast cancer care, supporting less invasive approaches while maintaining oncologic safety. This narrative review summarizes current practice and recent developments in radioguided breast and axillary surgery, from established molecular imaging workflows to emerging three-dimensional and intraoperative technologies. Modern breast cancer management is increasingly shaped by tumor biology and the widespread use of neoadjuvant systemic therapy, which is transforming surgical decision-making and driving a shift toward personalized, patient-tailored pathways. In this context, radioguided techniques help maintain procedural accuracy despite therapy-induced changes in breast and nodal anatomy, enabling reliable lesion localization and targeted management of the axilla. We discuss sentinel lymph node strategies and de-escalation concepts, including targeted axillary dissection (TAD) after neoadjuvant therapy using marked nodes and selective removal approaches. We also review localization methods, including radioactive seed–based techniques, and the expanding role of molecular imaging–guided surgery to support intraoperative decision-making. Particular attention is paid to technologies aimed at improving surgical precision and margin assessment, including portable/freehand SPECT concepts and intraoperative PET/CT-based specimen imaging for immediate evaluation of excised tissue. Finally, we highlight how artificial intelligence and digital tools may enable workflow optimization, navigation, image interpretation, and decision support, accelerating the transition toward individualized treatment. Overall, integrating molecular information with real-time 3D guidance can help tailor breast and axillary management to each patient while reducing morbidity. Full article