International Journal of Computer Assisted Radiology and Surgery最新文献

Purpose: In the last decade, the development of Deep Learning and its variants, based on the application of artificial neural networks, has reinvigorated Artificial Intelligence (AI). As a result, many new applications of AI in medicine, especially Radiology, have been introduced. This resurgence in AI, and its diverse clinical and nonclinical applications throughout healthcare, requires a thorough understanding to reap the potential benefits and avoid the potential pitfalls.

Methods: To realize the full potential of AI in medicine, a highly coordinated approach should be undertaken to select, support and finance more highly focused AI projects. By studying and understanding the successes and failures, and strengths and limitations, of AI in Radiology, it is possible to seek and develop the most clinically relevant AI algorithms. The authors have reviewed their clinical practice regarding the use of AI to determine applications in which AI can add both clinical and remunerative benefits.

Results: Review of our policies and applications regarding AI in the Department of Radiology emphasized that, at the time of this writing, AI has been useful in the detection of specific clinical entities for which the AI algorithms have been designed. In addition to helping to reduce diagnostic errors, AI offers an important opportunity to prioritize positive cases, such as pulmonary embolism or intracranial hemorrhage. It has become apparent that the detection of certain conditions, such as incidental and unsuspected cerebral aneurysms can be used to initiate a variety of patient-oriented activities. Finding an unsuspected brain aneurysm is not only of clinical importance to the patient, but the required clinical workup and management of the patient can help generate reimbursement that helps defray the cost of AI implementations. A program for screening, clinical management, and follow-up, facilitated by the AI detection of incidental brain aneurysms, has been implemented at our multi-hospital healthcare system.

Conclusion: We feel that it is possible to avoid missed opportunities for AI in Radiology and create AI tools to enhance medical wisdom and improve patient care, within a fiscally responsive environment.

Purpose: In mandibular reconstructive surgery with free fibula flap, 3D-printed patient-specific cutting guides are the current state of the art. Although these guides enable accurate transfer of the virtual surgical plan to the operating room, disadvantages include long waiting times until surgery and the inability to change the virtual plan intraoperatively in case of tumor growth. Alternatively, (electromagnetic) surgical navigation combined with a non-patient-specific cutting guide could be used, requiring accurate image-to-patient registration. In this phantom study, we evaluated the accuracy of a hybrid registration method for the fibula and the additional error that is caused by navigating with a prototype of a novel non-patient-specific cutting guide to virtually planned osteotomy planes.

Methods: The accuracy of hybrid registration and navigation was assessed in terms of target registration error (TRE), angular difference, and length difference of the intended fibula segments using three 3D-printed fibular phantoms with assessment points on osteotomy planes. Using electromagnetic tracking, hybrid registration was performed with point registration followed by surface registration on the lateral fibular surface. The fibula was fixated in the non-patient-specific cutting guide to navigate to planned osteotomy planes after which the accuracy was assessed.

Results: Registration was achieved with a mean TRE, angular difference, and segment length difference of 2.3 ± 0.9 mm, 2.1 ± 1.4°, and 0.3 ± 0.3 mm respectively after hybrid registration. Navigation with the novel cutting guide increased the length difference (0.7 ± 0.6 mm), but decreased the angular difference (1.8 ± 1.3°).

Conclusion: Hybrid registration showed to be a feasible and noninvasive method to register the fibula in phantom setup and could be used for electromagnetically navigated osteotomies with a novel non-patient-specific cutting guide. Future studies should focus on testing this registration method in clinical setting.

Purpose: Current training methods for surgical trainees are inadequate because they are costly, low-fidelity, or have a low skill ceiling. This work aims to expand available virtual reality training options by developing a VR trainer for straight coloanal anastomosis (SCA), one of the Colorectal Objective Structured Assessment of Technical Skills (COSATS) tasks.

Methods: We developed a VR-based SCA simulator to evaluate trainees based on their performance. To increase the immersiveness, alongside the VR headset, we used haptics as the primary method of interaction with the simulation. We also implemented objective performance metrics to evaluate trainee performance throughout the simulation.

Results: We presented our performance metrics to 27 participants for an Expert Consensus Survey (5-point Likert scale) and created weights for our metrics. The weighted average scores for the 24 task-specific metrics ranged from 3.5 to 5. Additionally, for the general metrics, the scores spanned from 3.3 to 4.6. In the second phase of our study, we conducted a study with 16 participants (novice n = 9, expert n = 7). Based on the performance, experts outperformed novices by 8.56% when referring to the total score (p = 0.0041). Three of the measurable metrics, purse suture (p = 0.0797), retracting the anvil (p = 0.0738), and inserting the colonoscope (p = 0.0738) showed a significant difference between experts and novices. Experts were smoother with their hand motions by 3.67% per second and took 70.77% longer paths to complete the same tasks.

Conclusion: We created a high-fidelity coloanal anastomosis VR simulator. The simulator runs in real-time while allowing high immersion with a VR headset, deformable bodies, and a haptic device while providing objective feedback through performance metrics. Experts obtained higher scores throughout the simulation, including the quiz to demonstrate procedural knowledge, the metrics to demonstrate experience in steps/procedure, and control of their basic surgical skills and hand movements.

Purpose: Accurately quantifying the rupture risk of unruptured intracranial aneurysms (UIAs) is crucial for guiding treatment decisions and remains an unmet clinical challenge. Computational Flow Dynamics and morphological measurements have been shown to differ between ruptured and unruptured aneurysms. It is not clear if these provide any additional information above routinely available clinical observations or not. Therefore, this study investigates whether incorporating image-derived features into the established PHASES score can improve the classification of aneurysm rupture status.

Methods: A cross-sectional dataset of 170 patients (78 with ruptured aneurysm) was used. Computational fluid dynamics (CFD) and shape analysis were performed on patients' images to extract additional features. These derived features were combined with PHASES variables to develop five ridge constrained logistic regression models for classifying the aneurysm rupture status. Correlation analysis and principal component analysis were employed for image-derived feature reduction. The dataset was split into training and validation subsets, and a ten-fold cross validation strategy with grid search optimisation and bootstrap resampling was adopted for determining the models' coefficients. Models' performances were evaluated using the area under the receiver operating characteristic curve (AUC).

Results: The logistic regression model based solely on PHASES achieved AUC of 0.63. All models incorporating derived features from CFD and shape analysis demonstrated improved performance, reaching an AUC of 0.71. Non-sphericity index (shape variable) and maximum oscillatory shear index (CFD variable) were the strongest predictors of a ruptured status.

Conclusion: This study demonstrates the benefits of integrating image-based fluid dynamics and shape analysis with clinical data for improving the classification accuracy of aneurysm rupture status. Further evaluation using longitudinal data is needed to assess the potential for clinical integration.

Purpose: Although the literature shows that robotic assistance can support the surgeon, robotic systems are not widely spread in clinics. They often incorporate large robotic arms adopted from the manufacturing industry, imposing safety hazards when in contact with the patient or surgical staff. We approached this limitation with a modular dual robot consisting of an ultra-lightweight carrier robot for rough prepositioning and small, highly dynamic, application-specific, interchangeable tooling robots.

Methods: A formative usability study with N = 10 neurosurgeons was conducted using a prototype of a novel tooling robot for laminectomy to evaluate the system's usability. The participants were asked to perform three experiments using the robotic system: (1) prepositioning with the carrier robot and milling into (2) a block phantom as well as (3) a spine model.

Results: All neurosurgeons could perform a simulated laminectomy on a spine phantom using the robotic system. On average, they rated the usability of this first prototype already between good and excellent (SUS-Score above 75%). Eight out of the ten participants preferred robotic-assisted milling over manual milling. For prepositioning, the developed haptic guidance showed significantly higher effectiveness and efficiency than visual navigation.

Conclusion: The proposed dual robot system showed the potential to increase safety in the operating room because of the synergistic hands-on control and the ultra-lightweight design of the carrier robot. The modular design allows for easy adaptation to various surgical procedures. However, improvements are needed in the ergonomics of the tooling robot and the complexity of the virtual fixtures. The cooperative dual robot system can subsequently be tested in a cadaver laboratory and in vivo on animals.

Purpose: Carotid endarterectomy (CEA) is a surgical treatment for carotid artery stenosis. After CEA, some patients experience cardiovascular events (myocardial infarction, stroke, etc.); however, the prognostic factor has yet to be revealed. Therefore, this study explores the predictive factors in pathological images and predicts cardiovascular events within one year after CEA using pathological images of carotid plaques and patients' clinical data.

Method: This paper proposes a two-step method to predict the prognosis of CEA patients. The proposed method first computes the pathological risk score using an anomaly detection model trained using pathological images of patients without cardiovascular events. By concatenating the obtained image-based risk score with a patient's clinical data, a statistical machine learning-based classifier predicts the patient's prognosis.

Results: We evaluate the proposed method on a dataset containing 120 patients without cardiovascular events and 21 patients with events. The combination of autoencoder as the anomaly detection model and XGBoost as the classification model obtained the best results: area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, and F1-score were 81.9%, 84.1%, 79.1%, 86.3%, and 76.6%, respectively. These values were superior to those obtained using pathological images or clinical data alone.

Conclusion: We showed the feasibility of predicting CEA patient's long-term prognosis using pathological images and clinical data. Our results revealed some histopathological features related to cardiovascular events: plaque hemorrhage (thrombus), lymphocytic infiltration, and hemosiderin deposition, which will contribute to developing preventive treatment methods for plaque development and progression.

Purpose: Percutaneous puncture is a common interventional procedure, and its effectiveness is influenced by the insertion force of the needle. To optimize outcomes, we focus on reducing the peak force of the needle in the skin, aiming to apply this method to other tissue layers.

Methods: We developed a clinical puncture system, setting and measuring various variables. We analyzed their effects, introduced admittance control, set thresholds, and adjusted parameters. Finally, we validated these methods to ensure their effectiveness.

Results: Our system meets application requirements. We assessed the impact of various variables on peak force and validated the effectiveness of the new method. Results show a reduction of about 50% in peak force compared to the maximum force condition and about 13% compared to the minimum force condition. Finally, we summarized the factors to consider when applying this method.

Conclusion: To achieve peak force suppression, initial puncture variables should be set based on the trends in variable impact. Additionally, the factors of the new method should be introduced using these initial settings. When selecting these factors, the characteristics of the new method must also be considered. This process will help to better optimize peak puncture force.

Purpose: Observer-based scoring systems, or automatic methods, based on features or kinematic data analysis, are used to perform surgical skill assessments. These methods have several limitations, observer-based ones are subjective, and the automatic ones mainly focus on technical skills or use data strongly related to technical skills to assess non-technical skills. In this study, we are exploring the use of heart-rate data, a non-technical-related data, to predict values of an observer-based scoring system thanks to random forest regressors.

Methods: Heart-rate data from 35 junior resident orthopedic surgeons were collected during the evaluation of a meniscectomy performed on a bench-top simulator. Each participant has been evaluated by two assessors using the Arthroscopic Surgical Skill Evaluation Tool (ASSET) score. A preprocessing stage on heart-rate data, composed of threshold filtering and a detrending method, was considered before extracting 41 features. Then a random forest regressor has been optimized thanks to a randomized search cross-validation strategy to predict each score component.

Results: The prediction of the partially non-technical-related components presents promising results, with the best result obtained for the safety component with a mean absolute error of 0.24, which represents a mean absolute percentage error of 5.76%. The analysis of feature important allowed us to determine which features are the more related to each ASSET component, and therefore determine the underlying impact of the sympathetic and parasympathetic nervous systems.

Conclusion: In this preliminary work, a random forest regressor train on feature extract from heart-rate data could be used for automatic skill assessment and more especially for the partially non-technical-related components. Combined with more traditional data, such as kinematic data, it could help to perform accurate automatic skill assessment.

Purpose: Lower-limb muscle mass reduction and fatty degeneration develop in patients with knee osteoarthritis (KOA) and could affect their symptoms, satisfaction, expectation and functional activities. The Knee Society Scoring System (KSS) includes patient reported outcome measures, which is widely used to evaluate the status of knee function of KOA. This study aimed to clarify how muscle mass and fatty degeneration of the lower limb correlate with the KSS in patients with KOA.

Methods: This study included 43 patients with end-stage KOA, including nine males and 34 females. Computed tomography (CT) images of the lower limb obtained for the planning of total knee arthroplasty were utilized. Ten muscle groups were segmented using our artificial-intelligence-based methods. Muscle volume was standardized by dividing by their height squared. The mean CT value for each muscle group was calculated as an index of fatty degeneration. Bivariate analysis between muscle volume or CT values and KSS was performed using Spearman's rank correlation test. Multiple regression analysis was performed, and statistical significance was set at p  < 0.05.

Results: Bivariate analysis showed that the functional activity score was significantly correlated with the mean CT value of all muscle groups except the adductors and iliopsoas. Multiple regression analysis revealed that the functional activities score was significantly associated with the mean CT values of the gluteus medius and minimus muscles and the anterior and lateral compartments of the lower leg (β = 0.42, p = 0.01; β = 0.33, p = 0.038; and β = 0.37, p = 0.014, respectively).

Conclusion: Fatty degeneration, rather than muscle mass, in the lower-limb muscles was significantly associated with functional activities score of the KSS in patients with end-stage KOA. Notably, the gluteus medius and minimus and the anterior and lateral compartments of the lower leg are important muscles associated with functional activities.