Search Results (301)

Tissue-engineered scaffolds, particularly composite scaffolds composed of polymers combined with ceramics, bioactive glasses, or nanomaterials, play a vital role in regenerative medicine by providing structural and biological support for tissue repair. As scaffold designs grow increasingly complex, the need for non-invasive imaging modalities capable of monitoring scaffold integration, degradation, and tissue regeneration in real-time has become critical. This review summarizes current non-invasive imaging techniques used to evaluate tissue-engineered constructs, including optical methods such as near-infrared fluorescence imaging (NIR), optical coherence tomography (OCT), and photoacoustic imaging (PAI); magnetic resonance imaging (MRI); X-ray-based approaches like computed tomography (CT); and ultrasound-based modalities. It discusses the unique advantages and limitations of each modality. Finally, the review identifies major challenges—including limited imaging depth, resolution trade-offs, and regulatory hurdles—and proposes future directions to enhance translational readiness and clinical adoption of imaging-guided tissue engineering (TE). Emerging prospects such as multimodal platforms and artificial intelligence (AI) assisted image analysis hold promise for improving precision, scalability, and clinical relevance in scaffold monitoring. Full article

Adhesive capsulitis is a painful and progressive condition marked by significant limitations in shoulder mobility, particularly affecting external rotation. Although magnetic resonance imaging is regarded as the reference standard for assessing intra-articular structures, its high cost and limited availability present challenges in routine clinical use. In contrast, musculoskeletal ultrasound has emerged as an accessible, real-time, and cost-effective imaging modality for both the diagnosis and treatment guidance of adhesive capsulitis. This narrative review compiles and illustrates current evidence regarding the role of ultrasound, encompassing static B-mode imaging, dynamic motion analysis, contrast-enhanced techniques, and sonoelastography. Key sonographic features—such as thickening of the coracohumeral ligament, fibrosis in the axillary recess, and abnormal tendon kinematics—have been consistently associated with adhesive capsulitis and demonstrate favorable diagnostic performance. Advanced methods like contrast-enhanced ultrasound and elastography provide additional functional insights (enabling evaluation of capsular stiffness and vascular changes) which may aid in disease staging and prediction of treatment response. Despite these advantages, the clinical utility of ultrasound remains subject to operator expertise and technical variability. Limited visualization of intra-articular structures and the absence of standardized scanning protocols continue to pose challenges. Nevertheless, ongoing advances in its technology and utility standardization hold promise for the broader application of ultrasound in clinical practice. With continued research and validation, ultrasound is positioned to play an increasingly central role in the comprehensive assessment and management of adhesive capsulitis. Full article

Objectives: This pilot study evaluates the correlation between periodontal pocket depth (PPD) measurements obtained by manual probing and those derived from an AI-coupled ultrasound imaging device in periodontitis patients. Materials and Methods: Thirteen patients with periodontitis underwent ultrasonic probing with an AI engine for automated PPD measurements, followed by routine manual probing. Results: A total of 2088 manual and 1987 AI-based PPD measurements were collected. The mean PPD was 4.2 mm (range: 2–8 mm) for manual probing and 4.5 mm (range: 2–9 mm) for AI-based ultrasound, with a Pearson correlation coefficient of 0.68 (95% CI: 0.62–0.73). Discrepancies were noted in cases with inflammation or calculus. AI struggled to differentiate pocket depths in complex clinical scenarios. Discussion: Ultrasound imaging offers non-invasive, real-time visualization of periodontal structures, but AI accuracy requires further training to address image artifacts and clinical variability. Conclusions: The ultrasound device shows promise for non-invasive periodontal diagnostics but is not yet a direct alternative to manual probing. Further AI optimization and validation are needed. Clinical Relevance: This technology could enhance patient comfort and enable frequent monitoring, pending improvements in AI reliability. Full article

Background/Objectives: In the era of treat-to-target strategies in inflammatory bowel disease (IBD), transmural healing (TH) is gaining recognition as a promising therapeutic goal. TH has been associated with significantly better long-term outcomes, including reduced rates of hospitalization, surgery, and the need for therapy escalation. Cross-sectional imaging techniques, such as intestinal ultrasound (IUS), magnetic resonance imaging (MRI), and computed tomography enterography (CTE), offer a comprehensive, non-invasive means to assess this deeper level of healing. This review explores how TH is currently defined across various imaging modalities and evaluates the feasibility and cost-effectiveness of achieving TH with available therapies. Methods: A literature search was conducted across PubMed, Scopus, and Embase using keywords, including “transmural healing”, “intestinal ultrasonography”, “magnetic resonance imaging”, “computed tomography enterography”, “Crohn’s disease”, “ulcerative colitis”, and “inflammatory bowel disease”. Only English-language studies were considered. Results: Despite growing interest, there is no standardized definition of TH across imaging platforms. Among the modalities, IUS emerges as the most feasible and cost-effective tool, owing to its accessibility, accuracy (sensitivity 62–95.2%, specificity 61.5–100%), and real-time capabilities, though it does have limitations. Current advanced therapies induce TH in roughly 20–40% of patients, with no consistent differences observed between biologics and small molecules. However, TH has only been evaluated as a formal endpoint in a single randomized controlled trial to date. Conclusions: A unified and validated definition of transmural healing is critically needed to harmonize research and guide clinical decision-making. While TH holds promise as a meaningful treatment target linked to improved outcomes, existing therapies often fall short of achieving complete transmural resolution. Further studies are essential to clarify its role and optimize strategies for deep healing in IBD. Full article

Despite advances in surgery, chemotherapy, and radiation treatments for pancreatic ductal adenocarcinoma (PDAC), 5-year survival rates remain at nearly 11%. Cholangiocarcinoma, while not as severe, also possesses similar survival rates. Fewer than 20% of patients are surgical candidates at time of diagnosis; therefore, it is imperative that alternative therapies are effective for non-surgical patients. There are several thermal ablative techniques, including radiofrequency ablation (RFA), high-intensity focused ultrasound (HIFU), microwave ablation (MWA), alcohol ablation, stereotactic body radiotherapy (SBRT), cryoablation, irreversible electroporation (IRE), biliary intraluminal brachytherapy, and biliary photodynamic therapy (PDT). Emerging literature in animal models and human patients has demonstrated that endoscopic ultrasound (EUS)-guided RFA (EUS-RFA) prevents tumor progression through coagulative necrosis, protein denaturation, and activation of anticancer immunity in local and distant tumor tissue (abscopal effect). RFA treatment has been shown to not only reduce tumor-associated immunosuppressive cells but also increase functional T cells in distant tumor cells not treated with RFA. The remarkable ability to reduce tumor progression and promote tumor microenvironment (TME) remodeling makes RFA a very promising non-surgical therapy technique that has the potential to reduce mortality in this patient population. EUS-RFA offers superior precision and safety compared to other ablation techniques for pancreatic and biliary cancers, due to real-time imaging capabilities and minimally invasive nature. Future research should focus on optimizing RFA protocols, exploring combination therapies with chemotherapy or immunotherapy, and expanding its use in patients with metastatic disease. This review article will explore the current data and underlying pathophysiology of EUS-RFA while also highlighting the role of ablative therapies as a whole in immune activation response. Full article

This paper presents the PRONOBIS project, an ultrasound-only, robotically assisted, deep learning-based system for prostate scanning and biopsy treatment planning. The proposed system addresses the challenges of precise prostate segmentation, reconstruction and inter-operator variability by performing fully automated prostate scanning, real-time CNN-transformer-based image processing, 3D prostate reconstruction, and biopsy needle position planning. Fully automated prostate scanning is achieved by using a robotic arm equipped with an ultrasound system. Real-time ultrasound image processing utilizes state-of-the-art deep learning algorithms with intelligent post-processing techniques for precise prostate segmentation. To create a high-quality prostate segmentation dataset, this paper proposes a deep learning-based medical annotation platform, MedAP. For precise segmentation of the entire prostate sweep, DAF3D and MicroSegNet models are evaluated, and additional image post-processing methods are proposed. Three-dimensional visualization and prostate reconstruction are performed by utilizing the segmentation results and robotic positional data, enabling robust, user-friendly biopsy treatment planning. The real-time sweep scanning and segmentation operate at 30 Hz, which enable complete scan in 15 to 20 s, depending on the size of the prostate. The system is evaluated on prostate phantoms by reconstructing the sweep and by performing dimensional analysis, which indicates 92% and 98% volumetric accuracy on the tested phantoms. Three-dimansional prostate reconstruction takes approximately 3 s and enables fast and detailed insight for precise biopsy needle position planning. Full article

Pelvic organ prolapse (POP) is a common pelvic floor disorder with substantial impact on women’s quality of life, necessitating accurate and reproducible diagnostic methods. This study investigates the use of three-dimensional (3D) transperineal ultrasound, integrated with artificial intelligence (AI), to evaluate pelvic floor biomechanics and identify correlations between biometric parameters and prolapse severity. Thirty-seven female patients diagnosed with genital prolapse (mean age: 65.3 ± 10.6 years; mean BMI: 29.5 ± 3.8) were enrolled. All participants underwent standardized 3D transperineal ultrasound using the Mindray Smart Pelvic system, an AI-assisted imaging platform. Key biometric parameters—anteroposterior diameter, laterolateral diameter, and genital hiatus area—were measured under three functional states: rest, maximal Valsalva maneuver, and voluntary pelvic floor contraction. Additionally, two functional indices were derived: the distensibility index (ratio of Valsalva to rest) and the contractility index (ratio of contraction to rest), reflecting pelvic floor elasticity and muscular function, respectively. Statistical analysis included descriptive statistics and univariate correlation analysis using Pelvic Organ Prolapse Quantification (POP-Q) system scores. Results revealed a significant correlation between laterolateral diameter and prolapse severity across multiple compartments and functional states. In apical prolapse, the laterolateral diameter measured at rest and during both Valsalva and contraction showed positive correlations with POP-Q point C, indicating increasing transverse pelvic dimensions with more advanced prolapse (e.g., r = 0.42 to 0.58; p < 0.05). In anterior compartment prolapse, the same parameter measured during Valsalva and contraction correlated significantly with POP-Q point AA (e.g., r = 0.45 to 0.61; p < 0.05). Anteroposterior diameters and genital hiatus area were also analyzed but showed weaker or inconsistent correlations. AI integration facilitated real-time image segmentation and automated measurement, reducing operator dependency and increasing reproducibility. These findings highlight the laterolateral diameter as a strong, reproducible anatomical marker for POP severity, particularly when assessed dynamically. The combined use of AI-enhanced imaging and functional indices provides a novel, standardized, and objective approach for assessing pelvic floor dysfunction. This methodology supports more accurate diagnosis, individualized management planning, and long-term monitoring of pelvic floor disorders. Full article

Background: Intraoperative ultrasound (IOUS) is revolutionizing gynecologic oncology surgery by overcoming the limitations of traditional imaging and intraoperative assessment. Its real-time, high-resolution capabilities address critical needs in tumor localization, fertility preservation, refined intraoperative decisions, and complete cytoreduction. Methods: We reviewed clinical studies (1998–2024) evaluating IOUS applications, analyzing data on detection accuracy, surgical outcomes, and implementation challenges from peer-reviewed literature and institutional experiences. Results: IOUS demonstrates 88–93% sensitivity for subcentimeter metastases, refining surgical decisions in 25–40% of cases. Key outcomes include increased complete resection rates (68% to 87%), a 38% reduction in unnecessary lymphadenectomies, and successful fertility preservation in 92% of cases. Limitations include learning curves, 12% false-negative rate for micrometastases, and significant capital investment cost barriers. Conclusions: IOUS represents a transformative advance in precision surgery, improving both oncologic outcomes and quality of life. While standardization and accessibility challenges remain, ongoing technological innovations promise to solidify its role as a surgical standard. Full article

Background: Intraoperative ultrasound (IOUS) provides real-time imaging during brain tumor surgery but remains underused in brain metastasis resection. This study evaluates the effectiveness of 2D IOUS in improving the extent of resection compared to standard neuronavigation. Methods: We retrospectively analyzed 55 adult patients with brain metastases treated surgically at a single center. Patients were divided into two groups: IOUS-guided surgery (n = 20) and standard neuronavigation (n = 35). Gross total resection (GTR) was defined as the extent of resection > 96%, assessed volumetrically. Statistical analyses included chi-square tests, logistic regression, and ROC curve analysis. Results: GTR > 96% was achieved in 80% of IOUS-guided cases compared to 42.86% in the control group (p = 0.008). IOUS significantly increased the odds of achieving GTR (OR = 5.33, p = 0.011). Larger tumor volume reduced the likelihood of GTR (OR = 0.469, p = 0.025), but this effect was mitigated by IOUS use (interaction OR = 1.986, p = 0.044). The regression model showed excellent discrimination (AUC = 0.930, p < 0.001). Functional outcomes improved postoperatively in both groups. Conclusions: 2D IOUS significantly enhances the extent of resection in brain metastasis surgery, including that for larger tumors. Its accessibility, real-time feedback, and low cost support its wider adoption in neurosurgical practice, especially in settings with limited resources. Full article

Objective: Intestinal ultrasound (IUS) is increasingly valued as a noninvasive tool for inflammatory bowel disease (IBD) management, offering real-time, radiation-free assessment of bowel wall thickness, vascularity, and complications. While IUS is widely adopted in Europe, data on its use in Turkey is scarce. This study aims to address this gap. Methods: A nationwide, cross-sectional survey was conducted targeting 817 adult and 150 pediatric gastroenterologists in Turkey. The survey included 26 structured questions on demographics, familiarity with and use of IUS, and barriers to implementation. Results: A total of 191 gastroenterologists participated in this survey, with 56% being adult gastroenterologists (n = 107) and 44% pediatric gastroenterologists (n = 84). Regarding whether they participated in IUS training, 73% (n = 140) of the 191 respondents stated they had not received training. There were notable differences in how IUS was utilized among gastroenterologists: 29% (n = 31) of adult gastroenterologists performed IUS independently, compared to just 2% (n = 2) of pediatric gastroenterologists (p < 0.001). In total, 63% (n = 67) of adult gastroenterologists and 46% (n = 39) of pediatric gastroenterologists reported not using IUS. Altogether, 94% (n = 179) emphasized the necessity of educational opportunities, and 86% (n = 165) favored national guidelines. Conclusions: Our findings reveal that the current application of IUS in Turkey fails to correspond with its expected advantages in managing IBD. Limited educational opportunities are a major challenge, emphasizing the necessity for coordinated educational programs and national guidelines. The expanded adoption of the IUS might significantly improve Turkey’s management of IBD. What is known: Intestinal ultrasound (IUS) is a non-invasive, cost-effective, and reliable imaging method increasingly recognized for its utility in diagnosing and monitoring inflammatory bowel disease (IBD). What is new: This is the first national survey assessing the awareness, usage patterns, and barriers to the adoption of IUS among gastroenterologists in Turkey. The study highlights significant gaps in training opportunities while also identifying strategies to promote IUS integration into routine clinical practice. The findings may encourage similar efforts in other regions where IUS remains underutilized, ultimately improving IBD management and patient outcomes globally. Full article

Thoracic injuries account for a high percentage of combat casualty mortalities, with 80% of preventable deaths resulting from abdominal or thoracic hemorrhage. An effective method for detecting and triaging thoracic injuries is point-of-care ultrasound (POCUS), as it is a cheap and portable noninvasive imaging method. POCUS image interpretation of pneumothorax (PTX) or hemothorax (HTX) injuries requires a skilled radiologist, which will likely not be available in austere situations where injury detection and triage are most critical. With the recent growth in artificial intelligence (AI) for healthcare, the hypothesis for this study is that deep learning (DL) models for classifying images as showing HTX or PTX injury, or being negative for injury can be developed for lowering the skill threshold for POCUS diagnostics on the future battlefield. Three-class deep learning classification AI models were developed using a motion-mode ultrasound dataset captured in animal study experiments from more than 25 swine subjects. Cluster analysis was used to define the “population” based on brightness, contrast, and kurtosis properties. A MobileNetV3 DL model architecture was tuned across a variety of hyperparameters, with the results ultimately being evaluated using images captured in real-time. Different hyperparameter configurations were blind-tested, resulting in models trained on filtered data having a real-time accuracy from 89 to 96%, as opposed to 78–95% when trained without filtering and optimization. The best model achieved a blind accuracy of 85% when inferencing on data collected in real-time, surpassing previous YOLOv8 models by 17%. AI models can be developed that are suitable for high performance in real-time for thoracic injury determination and are suitable for potentially addressing challenges with responding to emergency casualty situations and reducing the skill threshold for using and interpreting POCUS. Full article

Chronic total occlusions (CTOs), defined as complete coronary artery blockages persisting for over three months, are frequently encountered in up to 25% of coronary angiograms. Although percutaneous coronary intervention (PCI) for CTO remains technically challenging, advancements in guidewires, microcatheters, re-entry devices, and intravascular imaging, along with the expertise of specialized operators, have significantly improved procedural success rates, now exceeding 90% in expert centers. While recent evidence, such as the SYNTAX II study, emphasizes the importance of complete revascularization, over half of CTO cases continue to be managed conservatively with optimal medical therapy (OMT), partly due to the limited high-quality randomized evidence supporting revascularization. Observational studies have demonstrated that successful CTO-PCI is associated with improved angina relief, quality of life, left ventricular function, and possibly long-term survival. Extended observational follow-up, such as the Korean and Canadian registries, suggests long-term reductions in cardiac and all-cause mortality with CTO revascularization. However, randomized controlled trials (RCTs) have primarily shown symptomatic benefit, with no consistent reduction in major adverse cardiac events (MACE) or mortality, likely due to limited sample sizes, short follow-up, and treatment crossovers. Various strategies, including the hybrid algorithm, guide CTO interventions by balancing antegrade and retrograde techniques based on lesion complexity. Imaging modalities such as coronary CT angiography and intravascular ultrasound play a pivotal role in planning and optimizing these procedures. Future innovations, such as real-time fusion imaging of CCTA with coronary angiography, may enhance lesion visualization and guidewire navigation. While current guidelines recommend CTO-PCI in selected symptomatic patients with demonstrable ischemia or viable myocardium, the decision should be individualized, incorporating anatomical feasibility, comorbidities, patient preferences, and input from a multidisciplinary Heart Team. Looking ahead, adequately powered RCTs with extended follow-up are essential to determine the long-term clinical impact of CTO-PCI on hard outcomes such as mortality and myocardial infarction. Full article

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article

Sonomyography (SMG) is a method of controlling upper-limb prostheses through an innovative human–machine interface by monitoring forearm muscle activity through ultrasonic imaging. Over the past two decades, SMG has shown promise, achieving over 90% accuracy in classifying hand gestures when combined with artificial intelligence, making it a viable alternative to electromyography (EMG). However, up to now, there are few reports of a system integrating SMG together with a prosthesis for testing on amputee subjects to demonstrate its capability in relation to daily activities. In this study, we developed a highly efficient human–machine interface algorithm for controlling a prosthetic hand with 6-DOF using a wireless and wearable ultrasound imaging probe. We first evaluated the accuracy of our model in classifying nine different hand gestures to determine its reliability and precision. The results from the offline study, which included ten healthy participants, indicated that nine different hand gestures could be classified with a success rate of 100%. Additionally, the developed controlling system was tested in real-time experiments on two amputees, using a variety of hand function test kits. The results from the hand function tests confirmed that the prosthesis, controlled by the SMG system, could assist amputees in performing a variety of hand movements needed in daily activities. Full article

The increasing demand for accurate and accessible medical imaging has driven efforts to develop technologies that overcome limitations associated with conventional imaging techniques, such as MRI and CT scans. This study presents the design and implementation of an electronic interface for acquiring signals from a piezoelectric ultrasound sensor with the aim of improving image reconstruction quality by addressing electromagnetic interference and speckle noise, two major factors that degrade image fidelity. The proposed interface is installed between the ultrasound transducer and acquisition system, allowing real-time signal capture without altering the medical equipment’s operation. Using a printed circuit board with 110-pin connectors, signals from individual piezoelectric elements were analyzed using an oscilloscope. Results show that noise amplitudes occasionally exceed those of the acoustic echoes, potentially compromising image quality. By enabling direct observation of these signals, the interface facilitates the future development of analog filtering solutions to mitigate high-frequency noise before digital processing. This approach reduces reliance on computationally expensive digital filtering, offering a low-cost, real-time alternative. The findings underscore the potential of the interface to enhance diagnostic accuracy and support further innovation in medical imaging technologies. Full article