Search Results (40)

Lateral elbow pain is a common condition often misattributed solely to tendinopathy, while subtle instability may represent a significant underlying cause. Traditional classifications of elbow instability primarily address traumatic or grossly unstable patterns, leaving minor forms underrecognized. Recent evidence has emphasized the role of the Radial-Lateral Collateral Ligament (R-LCL) in maintaining joint stability, and its elongation has been linked to Symptomatic Minor Instability of the Lateral Elbow (SMILE). This model describes a horizontal type of radiocapitellar instability, where ligamentous incompetence leads to compensatory overload of the extensor carpi radialis brevis, ultimately producing chronic pain. Advances in diagnostic tools—including dynamic ultrasound (HELP-US test), CT arthrography with the SMILE Index, and arthroscopic signs such as the Loose Collar Sign—have improved recognition of this condition. However, surgical controversies remain, particularly regarding the potential destabilizing role of lateral release in patients with unrecognized R-LCL pathology. Arthroscopic stabilization techniques, such as R-LCL plication or imbrication, have shown promising outcomes, offering pain relief and functional recovery with minimally invasive approaches. This review integrates anatomical, biomechanical, and clinical evidence into a structured diagnostic and therapeutic algorithm, aiming to reduce diagnostic uncertainty and guide tailored interventions. Recognition of microinstability, and, in particular, the SMILE model, is crucial to optimize management of patients with chronic lateral elbow pain refractory to conservative measures. Full article

Background and Objectives: The lateral pterygoid muscle (LPM) is typically described as a two-headed muscle within the infratemporal fossa. However, cadaveric and imaging studies have revealed substantial variability in the number of heads, insertion patterns, and relations to neurovascular structures. Materials and Methods: An observational study of 250 brain computed tomography angiographies (CTAs) was performed to assess LPM morphology. Additionally, a systematic review and meta-analysis were conducted in accordance with PRISMA 2020 and Evidence-based Anatomy guidelines. Pooled prevalence estimates were calculated with random-effects models. Results: The current study included 250 CTAs for the original study and 1702 muscles for the meta-analytic evidence. During the original study, the two-headed configuration was most common (74.4%), followed by three-headed (14%), one-headed (10.8%), and four-headed (0.8%) morphologies. Symmetry was observed in 75.2% of patients. Meta-analysis confirmed the predominance of the two-headed type (73.98%, 95% CI: 68.22–79.38), with three-headed (16.82%), one-headed (4.37%), and four-headed (<0.01%) variants occurring less frequently. Subgroup analyses showed no significant differences by study type or sample size, though European populations exhibited a higher prevalence of one-headed forms. Conclusions: The LPM demonstrates considerable morphological variability, extending beyond the traditional two-headed model. Recognition of these variants is essential for understanding temporomandibular joint function, interpreting imaging, and planning surgical or interventional procedures within the infratemporal fossa. Advanced imaging provides a reliable tool for individualized anatomical assessment, supporting safer clinical practice. Full article

Right heart failure (RHF) remains an under-recognized yet devastating condition in critically ill and chronic patients, frequently complicating cardiac surgery, pulmonary embolism, advanced heart failure, sepsis and left ventricular assist device (LVAD) implantation. Despite growing awareness, clinical decision making is still hampered by the complex pathophysiology, limitations in diagnosis and a fragmented therapeutic landscape. In recent years, progress in hemodynamic phenotyping, advanced echocardiographic and biomarker-based assessment, and the development of mechanical circulatory support (MCS) systems, including percutaneous and surgical right ventricle assist devices (RVAD), veno-arterial extracorporeal membrane oxygenation (V-A ECMO), Impella RP (right percutaneous) or BiPella (Impella CP/5.0/5.5 + Impella RP) has expanded the armamentarium for managing RHF. This review synthetizes current evidences on the anatomical, physiological and molecular underpinnings of RHF, delineates the distinction and continuum between acute and chronic forms and provides a comparative analysis of diagnostic tools and MCS strategies. By integrating mechanistic insights with emerging clinical frameworks, the review aims to support earlier recognition, tailored management and innovative therapeutic approaches for this high-risk population. Full article

Background/Objectives: Heterotopic cesarean scar pregnancy (HCSP) is an exceptionally rare and potentially life-threatening form of ectopic pregnancy, characterized by the coexistence of a viable intrauterine pregnancy and an ectopic implantation within a previous cesarean section scar. Its incidence has risen in recent years, primarily due to the increased rate of cesarean deliveries and the widespread use of assisted reproductive technologies (ART). This systematic review aims to provide a comprehensive synthesis of published evidence on HCSP, with a focus on epidemiology, diagnostic challenges, therapeutic strategies, complications, and maternal-fetal outcomes. Methods: A systematic literature search was conducted in PubMed, Scopus, and Web of Science up to May 2025, in accordance with PRISMA guidelines. Included studies comprised case reports, case series and retrospective reviews documenting confirmed HCSP cases. Data were extracted on clinical presentation, imaging, treatment approaches, outcomes, and complications. Results: Thirty studies reporting 40 confirmed HCSP cases were included. Transvaginal ultrasonography was the primary diagnostic tool, although diagnosis was often delayed by the presence of a viable intrauterine pregnancy. Management strategies included surgical intervention, local medical therapy and conservative approaches or expectant management. Maternal complications included hemorrhage and uterine rupture, while fetal outcomes were variable. In selected cases, intrauterine pregnancy continued to term. Conclusions: HCSP is a rare but high-risk obstetric entity requiring early recognition and multidisciplinary management. Prompt ultrasound-based diagnosis and individualized treatment can significantly reduce maternal morbidity and improve fetal outcomes. Further multicenter studies are warranted to establish standardized diagnostic and management protocols. Full article

Convolutional neural networks (CNNs) can efficiently extract image features and perform corresponding classification. Typically, the CNN architecture uses the softmax layer to map the extracted features to classification probabilities, and the cost function used for training is the cross-entropy loss. In this paper, we evaluate the influence of a number of representative composite cross-entropy loss functions on the learned feature space at the fully connected layer, when a target classification is introduced into a multi-class classification task. In addition, the accuracy and robustness of CNN models trained with different composite cross-entropy loss functions are investigated. Improved robustness is achieved by changing the loss between the input and the target classification. Preliminary experiments were conducted using ResNet-50 on the Cholec80 dataset for surgical tool recognition. Furthermore, the model trained with the proposed composite cross-entropy loss incorporating another target all-one classification demonstrates a 31% peak improvement in adversarial robustness. Adversarial training with target adversarial samples yields 80% robustness against PGD attack. This investigation shows that the careful choice of the loss function can improve the robustness of CNN models. Full article

Background: Cholesteatoma, characterized by the abnormal growth of keratinizing squamous epithelium in ectopic locations, most commonly arises in the middle ear. Its occurrence in the sinonasal tract is rare and presents significant diagnostic and management challenges. These lesions can lead to severe complications like bone erosion, intracranial involvement, and orbital spread. This narrative review aims to summarize the current knowledge on cholesteatomas in these regions, focusing on epidemiology, pathophysiology, diagnosis, and treatment. Methods: A comprehensive review of the English literature was conducted, focusing on reported cases of cholesteatomas in the nasal cavity and paranasal sinuses. This review examines key aspects, including epidemiological data, imaging findings, surgical strategies, and postoperative outcomes. The role of diagnostic tools, particularly computed tomography and diffusion-weighted magnetic resonance imaging, in distinguishing cholesteatomas from other sinonasal lesions is also discussed. Results: As of March 2025, 51 cases of paranasal sinus cholesteatoma were reported. The frontal sinus is the most commonly affected site, followed by the maxillary, ethmoid, and sphenoid sinuses. Diagnosis is often delayed due to nonspecific symptoms, such as nasal congestion and recurrent infections. Surgical excision is the primary treatment, with endoscopic techniques being favored for their minimally invasive nature. Recurrence remains a major concern, and although very rare, cases of squamous cell carcinoma have also been observed in association with cholesteatoma. Conclusions: Nasal and paranasal sinus cholesteatomas require early recognition and intervention to prevent complications. Advances in imaging and surgery have improved outcomes; however, further research is needed to refine therapies and understand disease mechanisms. Full article

Robot-assisted surgery has transformed the landscape of genitourinary cancer treatment, offering enhanced precision, reduced morbidity, and improved recovery compared to open or conventional laparoscopic approaches. As the field matures, a new generation of technological innovations is redefining the boundaries of what robotic systems can achieve. This narrative review explores the integration of artificial intelligence, advanced imaging modalities, augmented reality, and connectivity in robotic urologic oncology. The applications of machine learning in surgical skill evaluation and postoperative outcome predictions are discussed, along with AI-enhanced haptic feedback systems that compensate for the lack of tactile sensation. The role of 3D virtual modeling, intraoperative augmented reality, and fluorescence-guided surgery in improving surgical planning and precision is examined for both kidney and prostate procedures. Emerging tools for real-time tissue recognition, including confocal microscopy and Raman spectroscopy, are evaluated for their potential to optimize margin assessment. This review also addresses the shift toward single-port systems and the rise of telesurgery enabled by 5G connectivity, highlighting global efforts to expand expert surgical care across geographic barriers. Collectively, these innovations represent a paradigm shift in robot-assisted urologic oncology, with the potential to enhance functional outcomes, surgical safety, and access to high-quality care. Full article

Artificial intelligence (AI) has shown its potential to advance applications in various medical fields. One such area involves developing integrated AI-based systems to assist in laparoscopic surgery. Surgical tool detection and phase recognition are key components to develop such systems, and therefore, they have been extensively studied in recent years. Despite significant advancements in this field, previous image-based methods still face many challenges that limit their performance due to complex surgical scenes and limited annotated data. This study proposes a novel deep learning approach for classifying and localizing surgical tools in laparoscopic surgeries. The proposed approach uses a self-supervised learning algorithm for surgical tool classification followed by a weakly supervised algorithm for surgical tool localization, eliminating the need for explicit localization annotation. In particular, we leverage the Bidirectional Encoder Representation from Image Transformers (BEiT) model for tool classification and then utilize the heat maps generated from the multi-headed attention layers in the BEiT model for the localizing of these tools. Furthermore, the model incorporates class weights to address the class imbalance issue resulting from different usage frequencies of surgical tools in surgeries. Evaluated on the Cholec80 benchmark dataset, the proposed approach demonstrated high performance in surgical tool classification, surpassing previous works that utilize both spatial and temporal information. Additionally, the proposed weakly supervised learning approach achieved state-of-the-art results for the localization task. Full article

Background: Advancements in surgical care have made it possible to offer cardiac surgery to an older and frailer patient cohort. Frailty has been recognised as a prognostic indicator that impacts post-operative recovery and patient outcomes. The aim of this study is to identify frailty assessment tools, evaluate the impact of frailty on post-operative outcomes, and explore strategies to optimise care for frail patients undergoing cardiac surgery. Methods: A comprehensive literature search was performed across PubMed, MEDLINE, and SCOPUS to identify articles reporting post-operative outcomes related to frail patients undergoing cardiac surgery. Results: Measurement tools such as gait speed, the Clinical Frailty Scale, Fried frailty phenotype, deficit accumulation frailty index and the Short Physical Performance Battery can be used to assess frailty. Frailty has been reported to increase the risk of post-operative morbidity and mortality. Multiple studies have also reported the association between frailty and an increased length of intensive care unit and hospital stays, as well as an increased risk of post-operative delirium. It is important to perform a comprehensive frailty assessment and implement perioperative optimisation strategies to improve outcomes in this patient population. Pre-operative strategies that can be considered include adequate nutritional support, cardiac prehabilitation, and assessing patients using a multidisciplinary team approach with geriatric involvement. Post-operatively, interventions such as early recognition and treatment of post-operative delirium, nutrition optimisation, early planning for cardiac rehabilitation, and occupational therapy can support patients’ recovery and reintegration into daily activities. Conclusions: The early identification of frail patients during the perioperative period is essential for risk stratification and tailored management strategies to minimise the impact of frailty on outcomes following cardiac surgery. Full article

Dystocia, or difficult labor, is a common complication during parturition in cattle that poses substantial risks to both dam and fetus. When the incidence is high on a farm level, it is a significant economic burden for dairy and beef enterprises. This review paper presents a comprehensive roadmap strategy to enhance decision-making in the management of dystocia in cows. The strategy encompasses early recognition and assessment, utilization of advanced diagnostic tools, and a range of medical and surgical interventions tailored to specific maternal and fetal causes of dystocia. The roadmap also integrates preventive measures to reduce the incidence of dystocia through genetic selection and optimized nutrition. By addressing the key challenges in dystocia management, such as resource constraints, timely intervention, and the need for continuous education, this strategy aims to improve health outcomes for cows and calves and reduce economic losses. Implementing this structured approach can facilitate better preparedness, efficient resource utilization, and improved overall livestock management, thereby promoting the sustainability and productivity of the cattle industry and addressing animal welfare aspects. Full article

Surgical data analysis is crucial for developing and integrating context-aware systems (CAS) in advanced operating rooms. Automatic detection of surgical tools is an essential component in CAS, as it enables the recognition of surgical activities and understanding the contextual status of the procedure. Acquiring surgical data is challenging due to ethical constraints and the complexity of establishing data recording infrastructures. For machine learning tasks, there is also the large burden of data labelling. Although a relatively large dataset, namely the Cholec80, is publicly available, it is limited to the binary label data corresponding to the surgical tool presence. In this work, 15,691 frames from five videos from the dataset have been labelled with bounding boxes for surgical tool localisation. These newly labelled data support future research in developing and evaluating object detection models, particularly in the laparoscopic image data analysis domain. Full article

Biological bone materials, complex and anisotropic, require precise machining in surgeries. Bone drilling, a key technique, is susceptible to increased friction from tool wear, leading to excessive forces and high temperatures that can damage bone and surrounding tissues, affecting recovery. This study develops a monitoring platform to assess tool wear during bone drilling, employing an experimental setup that gathers triaxial force and vibration data. A recognition model using a bidirectional long short-term memory network (BI-LSTM) with a multi-head attention mechanism identified wear levels. This model, termed ABI-LSTM, was optimized and benchmarked against SVR, RNN, and CNN models. The results from implementing the ABI-LSTM-based monitoring system demonstrated its efficacy in detecting tool wear, thereby potentially reducing surgical risks such as osteonecrosis and drill breakage, and enhancing surgical outcomes. Full article

The safety and robustness of convolutional neural networks (CNNs) have raised increasing concerns, especially in safety-critical areas, such as medical applications. Although CNNs are efficient in image classification, their predictions are often sensitive to minor, for human observers, invisible modifications of the image. Thus, a modified, corrupted image can be visually equal to the legitimate image for humans but fool the CNN and make a wrong prediction. Such modified images are called adversarial images throughout this paper. A popular method to generate adversarial images is backpropagating the loss gradient to modify the input image. Usually, only the direction of the gradient and a given step size were used to determine the perturbations (FGSM, fast gradient sign method), or the FGSM is applied multiple times to craft stronger perturbations that change the model classification (i-FGSM). On the contrary, if the step size is too large, the minimum perturbation of the image may be missed during the gradient search. To seek exact and minimal input images for a classification change, in this paper, we suggest starting the FGSM with a small step size and adapting the step size with iterations. A few decay algorithms were taken from the literature for comparison with a novel approach based on an index tracking the loss status. In total, three tracking functions were applied for comparison. The experiments show our loss adaptive decay algorithms could find adversaries with more than a 90% success rate while generating fewer perturbations to fool the CNNs. Full article

Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA (Robotics Surgical Maneuvers) dataset provides 206 trials of common surgical training tasks performed with the da Vinci Research Kit (dVRK). In this work, we extend the ROSMA dataset with two annotated subsets: ROSMAT24, which contains bounding box annotations for instrument detection, and ROSMAG40, which contains high and low-level gesture annotations. We propose an annotation method that provides independent labels for the right-handed tools and the left-handed tools. For instrument identification, we validate our proposal with a YOLOv4 model in two experimental scenarios. We demonstrate the generalization capabilities of the network to detect instruments in unseen scenarios. On the other hand, for gesture segmentation, we propose two label categories: high-level annotations that describe gestures at a maneuvers level, and low-level annotations that describe gestures at a fine-grain level. To validate this proposal, we have designed a recurrent neural network based on a bidirectional long-short term memory layer. We present results for four cross-validation experimental setups, reaching up to a 77.35% mAP. Full article

Continuous monitoring of vital signs using a wireless wearable device was implemented in 2018 at a surgical care unit of an academic hospital. This study aimed at gaining insight into nurses’ and patients’ perspectives regarding the use and innovation of a continuous vital signs monitoring system, three years after its introduction. This qualitative study was performed in a surgical, non-intensive care unit of an academic hospital in 2021. Key-user nurses (nurses with additional training and expertise with the device) and patients were selected for semi-structured interviews, and nurses from the ward were selected for a focus group interview using a topic list. Transcripts of the audio tapes were deductively analysed using four dimensions for adoptions of information and communication technologies (ICT) devices in healthcare. The device provided feelings of safety for nurses and patients. Nurses and patients had a few issues with the device, including the size and the battery life. Nurses gained knowledge and skills in using the system for measurement and interpretations. They perceived the system as a tool to improve the recognition of clinical decline. The use of the system could be further developed regarding the technical device’s characteristics, nurses’ interpretation of the data and the of type of alarms, the information needs of patients, and clarification of the definition and standardization of continuous monitoring. Three years after the introduction, wireless continuous vital signs monitoring is the new standard of care according to the end-users at the general surgical ward. Full article