Intraoperative Neurophysiological Monitoring Market register 8.92% CAGR to reach USD 5.02 billion by 2035 Increasing demands, size, share, top players technology, trends, demands, sales, regions, and ...

Intraoperative Neurophysiological Monitoring Market register 8.92% CAGR to reach USD 5.02 billion by 2035 Increasing demands, size, share, top players technology, trends, demands, sales, regions, and ...

Intraoperative Neurophysiological Monitoring Market register 8.92% CAGR to reach USD 5.02 billion by 2035 Increasing demands, size, share, top players technology, trends, demands, sales, regions, and ...

A retrospective study found that combining neuronavigation with intraoperative MRI and neuromonitoring raised complete resection rates and extended survival in IDH-mutant low-grade gliomas invading

Mixed reality and augmented reality technologies are increasingly being explored for intraoperative visualization, navigation, and surgical decision support. However, translation into routine surgical practice will depend not only on technical accuracy but also on whether these systems are usable, physically tolerable, and robust in the operating room environment. In this Viewpoint, we highlight 3 human factors domains that warrant greater attention in mixed reality surgical guidance: physical characteristics, dexterity and motor demands, and environmental factors. Published work has repeatedly drawn attention to challenges, including ergonomic burden, limited field of view, headset weight, visual and perceptual constraints, fatigue, and interaction burdens associated with gesture and voice control. These issues are often compounded by environmental conditions, such as noise, glare, crowding, and occlusion in real clinical settings. Although mixed reality may improve spatial awareness and reduce attention shifts in some settings, these benefits remain constrained by persistent #usability, workflow, and environmental limitations. Viewed through a broader systems human factors lens, discussion remains concentrated mainly on user-device interaction and selected task-level issues, with comparatively less attention to team, organizational, training, and governance factors. We argue that future human factors research should move beyond technical validation alone and adopt a broader systems perspective that addresses user diversity, sterile and low-burden interaction, environmental resilience, and the wider conditions required for safe clinical integration. Such an approach is essential if mixed reality surgical guidance systems are to become safe, usable, and clinically adoptable.

Microscope video provides a rich source of information for artificial intelligence in ophthalmic surgery, but recognition accuracy alone does not establish clinical benefit. We examine video-based workflow understanding, skill assessment, intraoperative assistance, and their potential integration with multimodal sensing and supervised robotic action, with emphasis on cataract and vitreoretinal surgery. Public datasets support retrospective recognition of phases, instruments, anatomy, and selected aspects of technical performance.

Intraoperative Ct Market size is projected to reach US$ 2.56 Billion by 2034 from US$ 1.31 Billion in 2025. The market is anticipated to register a CAGR of 7.75% from 2026 to 2034. Rising demand for real-time surgical imaging and minimally invasive image-guided procedures drives market growth. Technological advances in mobile gantry designs and low-dose radiation scanning systems offer strong commercial prospects.

Background: Postoperative mortality remains a key indicator of surgical safety and quality. Traditional risk models based on static preoperative or intraoperative variables often fail to capture dynamic physiological changes during surgery and generalize poorly across surgical types. To address these limitations, we developed and validated a deep learning framework capable of learning heterogeneous temporal patterns from intraoperative signals while maintaining interpretability and adaptability. Objective: This study aimed to develop a mixture-of-experts–based model (Postoperative Risk Modeling Expert network, PRIME-MoE) to provide dynamic, surgery-adaptive postoperative in-hospital mortality risk prediction. The primary objective was to improve prediction accuracy, cross-database robustness, and interpretability across heterogeneous surgical populations and institutions. Methods: We retrospectively analyzed 173,990 surgical cases from the INSPIRE (Informative Surgical Patient Dataset for Innovative Research Environment) and HUAXI databases. PRIME-MoE used 4 one-layer unidirectional long short-term memory experts with dense softmax routing based on static surgical context. We evaluated discrimination, calibration, threshold-specific operating points, parameter count, graphics processing unit inference latency, routing patterns, and a sensitivity analysis that truncated the final 30 minutes of each operation without retraining. Results: PRIME-MoE achieved internal-test area under the receiver operating characteristic curves (AUROCs) of 93.8 (95% CI 92.3‐96.0) in INSPIRE and 90.7 (95% CI 87.1‐94.8) in HUAXI. Reciprocal evaluation retained discrimination (AUROCs 90.8 and 87.8) but showed imperfect calibration (Brier scores 0.035‐0.049; calibration slopes 0.28‐0.49), supporting site-specific recalibration. Graphics processing unit inference required a median of 11.4 ms (9.1-18.0; 3.3-45.3) per case. Without retraining, scoring 30 minutes before the recorded end of surgery retained AUROCs of 93.1 in INSPIRE and 90.2 in HUAXI. Conclusions: PRIME-MoE provides an interpretable framework for postoperative in-hospital mortality prediction and retained discrimination in reciprocal evaluation across 2 heterogeneous perioperative databases. The 30-minute analysis supports near-end-of-surgery scoring, but calibration was insufficient for direct deployment. Prospective validation, local recalibration, threshold selection, and workflow evaluation are required before clinical use.

Objectives To establish an intraoperative CT/MR fusion imaging (FI)-guided core needle biopsy (CNB) approach for head and neck lesions and to evaluate its diagnostic performance in comparison with…

Tumor-seeking fluorescent dyes enable precise lesion localization by recognizing overexpressed receptors, providing a critical adjunctive technology for cancer histopathology. However, tumor heterogeneity and the poor understanding of targeting mechanisms limit their efficacy. Here we engineer CETIF6a, a click chemistry-compatible heptamethine cyanine dye, for multi-cancer targeting, intraoperative histopathology, and proteome-wide target identification. CETIF6a demonstrates margin delineation across multiple cancer types ( > 90% concordance with H&E staining). Quantitative proteomics reveals that the dye targets 5-15 times more tumor-specific proteins than in paracancerous tissues. Synergistic pan-cancer targeting is achieved through 491 conserved tumor-enriched proteins involved in ribosomal, proteasomal, and metabolic pathways, effectively overcoming heterogeneity. Mechanistic studies confirm that CETIF6a emits bright fluorescence upon covalent binding to targets via nucleoph

Santis Health advances prostate cancer treatment through robotic surgery, intraoperative pathology, and multidisciplinary care. Its integrated approach prioritizes cancer control, urinary continence, sexual function, and recovery, demonstrating how surgical innovation and clinical expertise can improve patient-centered care and long-term outcomes. New York, NY, United States, November 2, 2026 -- Santis Health has received a 2026 Global Recognition Award in Innovation for its work in prostate cancer surgery, which combines specialized robotic practice, intra-operative pathology and a sustained focus on patient recovery.