Search Results (336)

Background: Accurate deformity classification and fusion-level planning are essential in adolescent idiopathic scoliosis (AIS) surgery and are traditionally guided by Cobb angle measurement and the Lenke system. Multimodal large language models (LLMs) (e.g., ChatGPT-4.0; Claude 3.7 Sonnet, Gemini 2.5 Pro, DeepSeek-R1-0528 Chat) are increasingly used for image interpretation despite limited validation for radiographic decision-making. This study evaluated the agreement and reproducibility of contemporary multimodal LLMs for AIS assessment compared with expert spine surgeons. Methods: This single-center retrospective study included 125 AIS patients (94 females, 31 males; mean age 14.8 ± 1.9 years) who underwent posterior instrumentation (2020–2024). Two experienced spine surgeons independently performed Lenke classification (including lumbar and sagittal modifiers) and selected fusion levels (UIV–LIV) on standing AP, lateral, and side-bending radiographs; discrepancies were resolved by consensus to establish the reference standard. The same radiographs were analyzed by four paid multimodal LLMs using standardized zero-shot prompts. Because LLMs showed inconsistent end-vertebra selection, LLM-derived Cobb angles lacked a common anatomical reference frame and were excluded from quantitative analysis. Agreement with expert consensus and test–retest reproducibility (repeat analyses one week apart) were assessed using Cohen’s κ. Evaluation times were recorded. Results: Surgeon agreement was high for Lenke classification (92.0%, κ = 0.913) and fusion-level selection (88.8%, κ = 0.879). All LLMs demonstrated chance-level test–retest reproducibility and very low agreement with expert consensus (Lenke: 1.6–10.2%, κ = 0.001–0.036; fusion: 0.8–12.0%, κ = 0.003–0.053). Claude produced missing outputs in 17 Lenke and 29 fusion-level cases. Although LLMs completed assessments far faster than surgeons (seconds vs. ~11–12 min), speed did not translate into clinically acceptable reliability. Conclusions: Current general-purpose multimodal LLMs do not provide reliable Lenke classification or fusion-level planning in AIS. Their poor agreement with expert surgeons and marked internal inconsistency indicate that LLM-generated interpretations should not be used for surgical decision-making or patient self-assessment without task-specific validation. Full article

Background and Objectives: Airway management and ventilation during laryngotracheal surgery represent some of the most challenging tasks in anesthesiology. The shared airway between the surgeon and anesthesiologist requires continuous coordination to ensure optimal oxygenation while maintaining an unobstructed surgical field. Materials and Methods: This narrative review is based on a comprehensive literature search of PubMed, Embase, Scopus, and Google Scholar, covering all publications from inception to 30 June 2025. The literature search was performed using a defined Boolean strategy and explicit inclusion/exclusion criteria, focusing on adult human subjects. The search included combinations of the terms “laryngotracheal surgery,” “airway management,” “ventilation strategies,” “jet ventilation,” “Tritube,” and “Flow Controlled Ventilation.” Only English-language studies focused on human subjects were included. Results: Traditional ventilation strategies, such as apneic oxygenation and jet ventilation, remain widely used but present limitations in terms of gas exchange efficiency, risk of barotrauma, and surgical interference. In recent years, new devices and ventilation modes—particularly the Tritube^® combined with Flow-Controlled Ventilation—have emerged as promising alternatives. These approaches allow continuous ventilation with minimal airway diameter, improving surgical access and patient safety. FCV’s potential to optimize gas exchange and reduce mechanical power is physiologically compelling, but its supporting evidence remains limited and heterogeneous, primarily consisting of small, single-center studies and case series. Conclusions: Optimal airway and ventilation management in laryngotracheal surgery requires individualized planning, technical expertise, and close interdisciplinary communication. This approach must integrate objective neuromuscular monitoring to ensure patient safety and include a comprehensive strategy for safe postoperative airway management and extubation. While emerging technologies have significantly expanded available options, their successful application depends on training, experience, and appropriate case selection. Further high-quality clinical studies are needed to standardize protocols and validate long-term outcomes of these innovative ventilation strategies. Full article

In recent years, we assisted the exploitation of Artificial Intelligence (AI) that invasively pervades in several instances of everyday life. The potential of this technology promises the automation of human tasks increasing accuracy and efficiency. The integration of AI systems in the orthopaedic field is becoming more and more a concrete reality, so this topic is gaining increasing interest by the scientific community. More and more authors are testing the power of AI in orthopaedics, exploiting the application in routine workflow, and asking if AI could improve clinical and surgical practice. In this brief narrative review, the state-of-art of AI in hip district orthopaedics is presented, particularly focusing on the application of AI tools in the context of radiological images, early diagnosis, clinical datasets, and around operative theatre. Possible future development of AI-hip pathology management is exposed too, and clear doubts about exploits of these tools in clinical practice are also exposed. Full article

Background/Objectives: Laparoscopic surgery has become the pre-eminent surgical approach for performing general surgical and gynecological operations, but it can lead to musculoskeletal disorder in surgeons. This study aimed to investigate the musculoskeletal demands of completing four core laparoscopic skills tasks amongst Obstetrics and Gynecology (O&G) and General Surgery (GS) trainees, recognizing that differences between specialties may create different ergonomic and muscular demands. Methods: Ten O&G and ten GS trainees both performed the same four tasks to evaluate their core laparoscopic skills whilst using electromyography (EMG) to assess the physical demand of each task in the trainee groups as a percent of maximum voluntary contraction. Results: O&G trainees had significantly higher muscle activity when completing a hand–eye coordination (HEC) task (167.9 ± 63.8 vs. 92.5 ± 31.3%, p = 0.019), bimanual coordination (BMC) task (205.6 ± 80.7 vs. 106.9 ± 47.0%, p = 0.017), and suturing (267.7 ± 121.6 vs. 122.2 ± 33.0%, p = 0.016) task in the right trapezius and deltoid muscle groups compared to GS trainees. No difference was observed between trainee groups in the laparoscopic camera navigation (LCN) task (p = 0.438). Conclusions: There appears to be increased muscular activity in O&G compared to GS trainees during the same simulated laparoscopic tasks. The findings should inform training policy around the optimization of ergonomics to minimize the risk of musculoskeletal disorder. Full article

Artificial Intelligence (AI) is rapidly transforming surgical care by enabling more accurate diagnosis and risk prediction, personalized decision-making, real-time intraoperative support, and postoperative management. Ongoing trends such as multi-task learning, real-time integration, and clinician-centered design suggest AI is maturing into a safe, pragmatic asset in surgical care. Yet, significant challenges, such as the complexity and opacity of many AI models (particularly deep learning), transparency, bias, data sharing, and equitable deployment, must be surpassed to achieve clinical trust, ethical use, and regulatory approval of AI algorithms in healthcare. Explainable Artificial Intelligence (XAI) is an emerging field that plays an important role in bridging the gap between algorithmic power and clinical use as surgery becomes increasingly data-driven. The authors reviewed current applications of XAI in the context of surgery—preoperative risk assessment, surgical planning, intraoperative guidance, and postoperative monitoring—and highlighted the absence of these mechanisms in Generative AI (e.g., ChatGPT). XAI will allow surgeons to interpret, validate, and trust AI tools. XAI applied in surgery is not a luxury: it must be a prerequisite for responsible innovation. Model bias, overfitting, and user interface design are key challenges that need to be overcome and will be explored in this review to achieve the integration of XAI into the surgical field. Unveiling the algorithm is the first step toward a safe, accountable, transparent, and human-centered surgical AI. Full article

Maximal safe surgical resection is a foundational principle in brain tumor surgery. To date, many intraoperative modalities have been developed to help facilitate the identification of brain tumor versus normal brain tissue so that surgical resection is maximized but limited to the boundaries of the tumor for preservation of neurological function. Of note, Raman spectroscopy has been adapted into one of these modalities because of its ability to provide rapid, non-destructive, label-free intraoperative evaluation of tumor borders and molecular classifications and help guide surgical decision-making in real time. In this review, we performed a literature review of the landmark studies incorporating Raman spectroscopy into neurosurgical care to highlight its current applications and limitations. In this modern day, Raman spectroscopy is able to detect tumor cells intraoperatively for primary glial neoplasms, meningiomas, and brain metastases with greater than 90% accuracy. For glioma surgery, a major recent advancement is the ability to detect different mutations intraoperatively, specifically IDH, 1p19q co-deletion, and ATRX, given their implications on survival and how much extent of resection should be ideally achieved. With recent advancements in artificial intelligence and their integration into stimulated Raman histology, many of these tasks can be completed in as fast as ~10 s and on average 2–3 min. Despite the incorporation of artificial intelligence, spectral data can still be heavily influenced by background noise, and its preprocessing has significant variability across platforms, which can impact the accuracy of results. Overall, Raman spectroscopy has significantly changed the intraoperative workflow of brain tumor surgery, and this review highlights the capabilities that neurosurgeons can currently take advantage of in their practice, the existing data to support it, and the areas that researchers can further optimize to improve accuracy and patient outcomes. Full article

Background: Language mapping is a critical component of epilepsy surgery, as postoperative language deficits can significantly impact patients’ quality of life. Electrical stimulation mapping has emerged as a valuable tool for identifying eloquent areas of the brain and minimising post-surgical language deficits. However, recent studies have shown that language deficits can occur despite language mapping, potentially due to variability in stimulation techniques and language task selection. The validity of specific linguistic tasks for mapping different cortical regions remain inadequately characterised. Objective: To systematically evaluate the validity of linguistic tasks used during electrical cortical stimulation (ECS) for language mapping in epilepsy surgery, analyse task-specific responses across cortical regions, and assess current evidence supporting optimal task selection for different brain areas. Methods: Following PRISMA [2020] guidelines, a systematic literature search was conducted in PubMed and Scopus covering articles published from January 2013 to November 2025. Studies on language testing with electrical cortical stimulation in epilepsy surgery cases were screened. Two reviewers independently screened 956 articles, with 45 meeting the inclusion criteria. Data extraction included language tasks, stimulation modalities (ECS, SEEG, ECoG, DECS), cortical regions and language error types. Results: Heterogeneity in language testing techniques across various centres was identified. Visual naming deficits were primarily associated with stimulation of the posterior and basal temporal regions, fusiform gyrus, and parahippocampal gyrus. Auditory naming elicited impairments in the posterior superior and middle temporal gyri, angular gyrus, and fusiform gyrus. Spontaneous speech errors varied, with phonemic dysphasic errors linked to the inferior frontal and supramarginal gyri, and semantic errors arising from superior temporal and perisylvian parietal regions. Conclusions: Task-specific language mapping reveals distinct cortical specialisations, with systematic patterns emerging across studies. However, marked variability in testing protocols and inadequate standardisation limit reproducibility and cross-centre comparisons. Overall, refining and standardising the language task implementation process could lead to improved outcomes, ultimately minimising resection-related language impairment. Future research should validate task–region associations through prospective multicentre studies with long-term outcome assessment. Full article

Catheter three-dimensional (3D) position reconstruction is a technology that reconstructs spatial positions from multiple two-dimensional (2D) images. It plays a pivotal role in endovascular surgical navigation, guiding surgical catheters during minimally invasive procedures within vessels. While deep learning approaches have demonstrated significant potential for catheter 3D reconstruction, their clinical applicability is limited due to the lack of annotated datasets. In this work, we propose a synthetic data generation pipeline coupled with a multi-task deep learning framework for simultaneous catheter 3D reconstruction and segmentation from biplanar 2D X-ray images. Our pipeline begins with a novel synthetic data generation methodology that creates realistic catheter datasets with precise ground truth annotations. We next present a combined catheter segmentation and 3D reconstruction architecture, utilizing shared encoder features, in the context of a multi-task deep learning framework. Finally, our work demonstrates the effectiveness of the synthetic data generation method for training deep learning models for 3D reconstruction and segmentation of medical instruments. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly applied across the perioperative continuum, with potential benefits in efficiency, personalization, and patient safety. Unfortunately, most such tools are developed in isolation, limiting their clinical utility. Multi-Agent Systems for Healthcare (MASH), in which autonomous AI agents coordinate tasks across multiple domains, may provide the necessary framework for integrated perioperative care. This critical review synthesizes current AI applications in anesthesiology and considers their integration within a MASH architecture. This is the first review to advance MASH as a conceptual and practical framework for anesthesiology, uniquely contributing to the AI discourse by proposing its potential to unify isolated innovations into adaptive and collaborative systems. Methods: A critical review was conducted using PubMed and Google Search to identify peer-reviewed studies published between 2015 and 2025. The search strategy combined controlled vocabulary and free-text terms for AI, anesthesiology, perioperative care, critical care, and pain management. Results were filtered for randomized controlled trials and clinical trials. Data were extracted and organized by perioperative phase. Results: The 16 studies (6 from database search, 10 from prior work) included in this review demonstrated AI applications across the perioperative timeline. Preoperatively, predictive models such as POTTER improved surgical risk stratification. Intraoperative trials evaluated systems like SmartPilot and Navigator, enhancing anesthetic dosing and physiologic stability. In critical care, algorithms including NAVOY Sepsis and VentAI supported early detection of sepsis and optimized ventilatory management. In pain medicine, AI assisted with opioid risk assessment and individualized pain-control regimens. While these trials demonstrated clinical utility, most applications remain domain-specific and unconnected from one another. Conclusions: AI has broad potential to improve perioperative care, but its impact depends on coordinated deployment. MASH offers a unifying framework to integrate diverse agents into adaptive networks, enabling more personalized anesthetic care that is safer and more efficient. Full article

An incision point acts as the pivot point when a minimally invasive surgery procedure is applied. The assistive robot arms employed for such operation must have the capability to perform a remote center of motion (RCM) at this pivot point. Other than designing RCM mechanisms, a common practice is to use a readily available spatial serial robot arm and control it to impose this RCM constraint. When this assistive robot is required to be backdriven by the surgeon, the relation between the interaction forces/moments and the motion with RCM constraint becomes challenging. This paper carefully formulates a hybrid position/force control scheme for this relationship when any readily available robot arm that is coupled with a force/torque sensor is used for an RCM task. The verification of the formulation is carried out on a readily available robot arm by implementing the additional constraints that are derived from a surgical robot application. Full article

In parallel with technical training on knowledge and skills of task-specific medical or surgical procedures, wide arrays of soft skills training would contribute to obstetric safety in the contemporary healthcare setting. This article, as a service evaluation, explored the effect of a specialty-based Crew Resource Management (CRM) training series that transforms the concept of human factors into sustainable measures in fostering clinical safety culture of the Department of Obstetrics and Gynaecology (O&G) in the Queen Elizabeth Hospital. Within the last decade, a tri-phasic programme has been implemented by an inter-professional workgroup which consists of a consultant anaesthesiologist, medical specialists and departmental operations manager from O&G, a nurse simulation specialist, hospital administrators, and a research psychologist. (1) Phase I identified different patterns of attitudinal changes (in assertiveness, communication, leadership, and situational awareness, also known as “ACLS”) between doctors and nurses and between generic and specialty-based sessions for curriculum planning. (2) Phase II evaluated how these specific behaviours changed over 3 months following CRM training tailored for frontline professionals in O&G. (3) Phase III examined the coping style in conflict management and the level of sustainability in self-efficacy over 3 months following specialty-based CRM training. The findings showed the positive impacts of O&G CRM training on healthcare professionals’ increased attitude and behaviour in “ACLS” by 22.7% at a p < 0.05 level, character strengths in conflict management, and non-inferior or sustained level of self-efficacy under tough conditions in the clinical setting up to 3 months after training. As a way forward, incorporating a scenario-based O&G CRM programme into existing skills-based training is expected to change service framework with an innovative approach. In addition, exploring actual clinical outcomes representing a higher level of organisational impacts can be a strategic direction for further studies on the effect of this practical and educational approach on obstetric safety culture. Full article

Robot-assisted radical prostatectomy (RARP) has become the most prevalent treatment for patients with organ-confined prostate cancer. Despite superior outcomes, suboptimal vesicourethral anastomosis (VUA) may lead to serious complications, including urinary leakage, prolonged catheterization, and extended hospitalization. A precise localization of both the surgical needle and the surrounding vesical and urethral tissues to coadapt is needed for fine-grained assessment of this task. Nonetheless, the identification of anatomical structures from endoscopic videos is difficult due to tissue distortions, changes in brightness, and instrument interferences. In this paper, we propose and compare two Deep Learning (DL) pipelines for the automatic segmentation of the mucosal layers and the suturing needle in real RARP videos by exploiting different architectures and training strategies. To train the models, we introduce a novel, annotated dataset collected from four VUA procedures. Experimental results show that the nnU-Net 2D model achieved the highest class-specific metrics, with a Dice Score of 0.663 for the mucosa class and 0.866 for the needle class, outperforming both transformer-based and baseline convolutional approaches on external validation video sequences. This work paves the way for computer-assisted tools that can objectively evaluate surgical performance during the critical phase of suturing tasks. Full article

Evidence of possible cerebellar involvement in spatial processing, place learning and other types of higher order functions comes mainly from clinical observations, as well as from mutant mice and lesion studies. The latter, in particular, have reported deficits in spatial learning and memory following surgical or neurotoxic cerebellar ablation. However, the low specificity of such manipulations has often made it difficult to precisely dissect the cognitive components of the observed behaviors. Likewise, due to conflicting data coming from lesion studies, it has not been possible so far to conclusively address whether a cerebellar dysfunction is sufficient per se to induce learning deficits, or whether concurrent damage to other regulatory structure(s) is necessary to significantly interfere with cognitive processing. In the present study, the immunotoxin 192 IgG-saporin, selectively targeting cholinergic neurons in the basal forebrain and a subpopulation of cerebellar Purkinje cells, was administered to adult rats bilaterally into the basal forebrain nuclei, the cerebellar cortices or both areas combined. Additional animals underwent injections of the toxin into the lateral ventricles. Starting from two–three weeks post-lesion, the animals were tested on paradigms of motor ability as well as spatial learning and memory and then sacrificed for post-mortem morphological analyses. All lesioned rats showed no signs of ataxia and no motor deficits that could impair their performance in the water maze task. The rats with discrete cerebellar lesions exhibited fairly normal performance and did not differ from controls in any aspect of the task. By contrast, animals with double lesions, as well as those with 192 IgG-saporin given intraventricularly did manifest severe impairments in both reference and working memory. Histo- and immunohistochemical analyses confirmed the effects of the toxin conjugate on target neurons and fairly similar patterns of Purkinje cell loss in the animals with cerebellar lesion only, basal forebrain-cerebellar double lesions and bilateral intraventricular injections of the toxin. No such loss was by contrast seen in the basal forebrain-lesioned animals, whose Purkinje cells were largely spared and exhibited a normal distribution pattern. The results suggest important functional interactions between the ascending regulatory inputs from the cerebellum and those arising in the basal forebrain nuclei that would act together to modulate the complex sensory–motor and cognitive processes required to control whole body movement in space. Full article

The VR-based circular tracking movement evaluation system (CES) was developed to quantitatively assess visuomotor control. The virtual stick, a component of the CES, provides visual cues in the virtual environment and haptic feedback when holding the controller. This study examined the effects of stick presence and presentation order on control accuracy for distance, angle, and angular velocity. Twenty-seven participants (12 females; mean age 23.3 ± 2.3 years) performed tasks in the frontal plane followed by the sagittal plane. In each plane, the stick was visible for the first 1–3 revolutions and invisible for the subsequent 4–6 revolutions in the invisible condition, with the reverse order in the visible condition. In the invisible condition, control accuracy with the stick was 1.10 times higher for distance only in the sagittal plane, and 1.13 and 1.09 times higher for angle and angular velocity in the frontal plane, and 1.11 and 1.08 times higher in the sagittal plane. No significant differences were observed in the visible condition. The improved control accuracy when the stick was visible is likely due to enhanced precision in constructing the reference frame, internal models, body coordinates, and effective multisensory integration of visual and haptic information. Such visual information may enable fine control in virtual environment-based applications, including games and surgical simulations. Full article

Artificial intelligence (AI) and augmented intelligence have significant potential in oculoplastics, offering tools for diagnosis, treatment recommendations, and administrative efficiency. This article discusses current and potential applications of AI in ptosis, eyelid and conjunctival cancer, thyroid-associated orbitopathy (TAO), giant cell arteritis (GCA), and orbital fractures. AI-based programs can assist in screening, predicting surgical outcomes, and improving patient care through data-driven decisions. Privacy concerns, particularly with the use of facial and ocular photographs, require robust solutions, including blockchain, federated learning and steganography. Large generalizable datasets with adequate validation are crucial for future AI development. While AI can assist in clinical decision-making and administrative tasks, physician oversight remains critical to prevent potential errors. Large language models like ChatGPT also have the potential to counsel patients, although further validation is needed to ensure accuracy and patient safety. Ultimately, AI should be regarded as an augmentative tool that supports, rather than replaces, physician expertise in oculoplastic care. Full article