COMPARISONS BETWEEN DIGITAL-GUIDED AND NONDIGITAL PROTOCOL IN IMPLANT PLANNING, PLACEMENT, AND RESTORATIONS: A SYSTEMATIC REVIEW AND META-ANALYSIS OF RANDOMIZED CONTROLLED TRIALS.

OBJECTIVES: The present study aimed to systematically review the current randomized clinical trials (RCTs) with respect to computer-aided design/computer-aided manufactured (CAD/CAM) techniques in the process of implant planning, placement, and rehabilitation. MATERIALS AND METHODS: Four independent reviewers conducted an electronic and manual literature search using several databases, including the National Library of Medicine (MEDLINE-PubMed), Cochrane Central Register of Controlled Trials (CENTRAL), and EMBASE. Articles were included if they were RCTs involving the interventions regarding the computer-guided impression, placement, and manufacturing process. The outcomes of interest include clinical and patient-reported outcomes and time efficiency. A meta-analysis was conducted to evaluate the time efficiency, pain severity, accuracy of implant placement, and postsurgery marginal bone level. RESULTS: A total of 39 and 25 articles were included in the qualitative and quantitative analysis, respectively. The results of the meta-analysis showed that significantly less time was spent performing the digital impression procedure than the conventional impression (P = .002). In addition, the average adjustment time of the final prosthesis was significantly less than the nondigital fabricated prosthesis (P = .0005). Computer-guided groups reported significantly lower painkiller consumption compared to control groups (P = .03). CONCLUSIONS: Digital impressions and CAD/CAM procedures are time-saving and provide stable and predictable outcomes. Moreover, computer-guided surgery can effectuate an accurate implant placement and less postsurgery discomfort.

Pan-mediastinal neoplasm diagnosis via nationwide federated learning: a multicentre cohort study.

BACKGROUND: Mediastinal neoplasms are typical thoracic diseases with increasing incidence in the general global population and can lead to poor prognosis. In clinical practice, the mediastinum's complex anatomic structures and intertype confusion among different mediastinal neoplasm pathologies severely hinder accurate diagnosis. To solve these difficulties, we organised a multicentre national collaboration on the basis of privacy-secured federated learning and developed CAIMEN, an efficient chest CT-based artificial intelligence (AI) mediastinal neoplasm diagnosis system. METHODS: In this multicentre cohort study, 7825 mediastinal neoplasm cases and 796 normal controls were collected from 24 centres in China to develop CAIMEN. We further enhanced CAIMEN with several novel algorithms in a multiview, knowledge-transferred, multilevel decision-making pattern. CAIMEN was tested by internal (929 cases at 15 centres), external (1216 cases at five centres and a real-world cohort of 11 162 cases), and human-AI (60 positive cases from four centres and radiologists from 15 institutions) test sets to evaluate its detection, segmentation, and classification performance. FINDINGS: In the external test experiments, the area under the receiver operating characteristic curve for detecting mediastinal neoplasms of CAIMEN was 0·973 (95% CI 0·969-0·977). In the real-world cohort, CAIMEN detected 13 false-negative cases confirmed by radiologists. The dice score for segmenting mediastinal neoplasms of CAIMEN was 0·765 (0·738-0·792). The mediastinal neoplasm classification top-1 and top-3 accuracy of CAIMEN were 0·523 (0·497-0·554) and 0·799 (0·778-0·822), respectively. In the human-AI test experiments, CAIMEN outperformed clinicians with top-1 and top-3 accuracy of 0·500 (0·383-0·633) and 0·800 (0·700-0·900), respectively. Meanwhile, with assistance from the computer aided diagnosis software based on CAIMEN, the 46 clinicians improved their average top-1 accuracy by 19·1% (0·345-0·411) and top-3 accuracy by 13·0% (0·545-0·616). INTERPRETATION: For mediastinal neoplasms, CAIMEN can produce high diagnostic accuracy and assist the diagnosis of human experts, showing its potential for clinical practice. FUNDING: National Key R&D Program of China, National Natural Science Foundation of China, and Beijing Natural Science Foundation.

Heterogeneous trajectories of kinesiophobia and their effects on rehabilitation outcomes after total knee arthroplasty: a prospective cohort study.

BACKGROUND: Kinesiophobia is one of the most common and aversive psychological phenomena among patients after total knee arthroplasty (TKA). This study aimed to identify trajectories of kinesiophobia, examine factors distinguishing these trajectories, and clarify the association between trajectories of kinesiophobia and rehabilitation outcomes. METHODS: In this prospective cohort study, the patients who underwent TKA were recruited between December 2021 and April 2022 from three orthopedic wards of a tertiary hospital in China. Kinesiophobia was measured using the Tampa Scale for Kinesiophobia at baseline (T0), and then at 1 month (T1) and 3 months (T2) after TKA to perform latent class growth analysis. Meanwhile, rehabilitation outcomes were assessed at 3 months after TKA, using the Kessler Psychological Distress Scale, the Hospital for Special Surgery-Knee Scale, Barthel Index, and the Impact on Participation and Autonomy questionnaire. RESULTS: The four kinesiophobia trajectories identified were as follows: low stable group (n = 120), rapid recovering group (n = 31), slow recovering group (n = 48), and stable moderate group (n = 58). Body mass index, employment status, heart disease, and pain degree significantly predicted trajectory groups (all p < 0.05). Analysis of variance revealed significant differences between the four kinesiophobia trajectories concerning all rehabilitation outcomes, except for the activities of daily living. CONCLUSION: Distinct kinesiophobia trajectories were identified, and nurses should assess the kinesiophobia of patients after TKA in the early phase. Patients in the slow recovering group are worthy of a specific focus because of their poor recovery after undergoing TKA. As important sources of psychosocial care, nurses need to customize psychological interventions for patients after TKA depending on each kinesiophobia trajectory.

Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms.

Background: Developing prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is inaccurate and may not adequately utilize large amounts of medical data. Machine learning is a promising way to predict the benefit of immunotherapy from individual data by individuating the most important features from genomic data and clinical characteristics. Methods: Machine learning was applied to identify a list of candidate genes that may predict immunotherapy benefits using data from the published cohort of 853 patients with NSCLC. We used XGBoost to capture nonlinear relations among many mutation genes and ICI benefits. The value of the derived machine learning-based mutation signature (ML-signature) on immunotherapy efficacy was evaluated and compared with the tumor mutational burden (TMB) and other clinical characteristics. The predictive power of ML-signature was also evaluated in independent cohorts of patients with NSCLC treated with ICI. Results: We constructed the ML-signature based on 429 (training/validation = 8/2) patients who received immunotherapy and extracted 88 eligible predictive genes. Additionally, we conducted internal and external validation with the utility of the OAK+POPLAR dataset and independent cohorts, respectively. This ML-signature showed the enrichment in immune-related signaling pathways and compared to TMB, ML-signature was equipped with favorable predictive value and stratification. Conclusion: Previous studies proposed no predictive difference between original TMB and modified TMB, and original TMB contains some genes with no predictive value. To demonstrate that fewer genetic tests are sufficient to predict immunotherapy efficacy, we used machine learning to screen out gene panels, which are used to calculate TMB. Therefore, we obtained the 88-gene panel, which showed the favorable prediction performance and stratification effect compared to the original TMB.

Symptom burden, family resilience, and functional exercise adherence among postoperative breast cancer patients.

Objective: This study examines the relationships among family resilience, functional exercise adherence, and symptom burden in postoperative breast cancer patients. Methods: In this cross-sectional study, participants consisted of 192 women with breast cancer who had undergone breast cancer surgery in two hospitals in Shandong Province, China. Family resilience, functional exercise adherence, and symptom burden were measured using the 32-item shortened Chinese version of the Family Resilience Assessment Scale (FRAS-C), Postoperative Functional Exercise Compliance Scale for Breast Cancer Patients, and the Chinese version of the M. D. Anderson Symptom Inventory (MDASI-C), respectively. Structural equation modeling was conducted to examine the path relationships among family resilience, functional exercise adherence, and symptom burden. Results: Family resilience and its subscales were significantly negatively correlated with symptom burden (r â€‹= â€‹-0.17 to -0.14, P â€‹< â€‹0.05), whereas positively correlated with functional exercise adherence (r â€‹= â€‹0.64 to 0.69, P â€‹< â€‹0.01). Functional exercise adherence was significantly positively correlated with symptom burden (r â€‹= â€‹-0.32 to -0.35, P â€‹< â€‹0.01). Family resilience indirectly affected symptom burden through functional exercise adherence (ߠ​= â€‹-0.319, 95% CI: -0.491, -0.169). Conclusions: Family resilience, as a positive psychological factor, could indirectly impact postoperative breast cancer patients' physical function. Specifically, family resilience can alleviate the patients' symptom burden by strengthening their functional exercise adherence. In addition to improving functional exercise adherence, nurses can also improve family resilience when helping to alleviate the symptom burden of postoperative breast cancer patients. Family resilience-based interventions could be implemented to alleviate the symptom burden among such patients.

Dyadic effects of family resilience on post-traumatic stress symptoms among breast cancer patients and their primary family caregivers: A cross-sectional study.

PURPOSE: The aims of this study were to verify actor and partner effects, by examining the effects of family resilience on post-traumatic stress symptoms (PTSS) among Chinese breast cancer patients and their primary family caregivers. METHODS: In this cross-sectional study, 104 breast cancer patients (age range 20-75, Mean = 47, Standard Deviation = 10), and their principal caregivers (n = 104), were recruited from a comprehensive cancer center of a public hospital in China. The patients and their caregivers self-reported sociodemographic, family resilience, and PTSS factors. The actor-partner interdependence model were adopted to examine whether the patients and caregivers' perceived family resilience could contribute to their own ("actor effect") and each other's ("partner effect") PTSS. RESULTS: There were significant correlations between patients' and caregivers' shortened Chinese version of Family Resilience Assessment Scale scores (r = 0.58, p < 0.01) and Post-traumatic Stress Disorder Checklist-Civilian Version scores (r = 0.69, p < 0.01). Caregivers' perceived family resilience was negatively related to their PTSS (actor effect), and the patients' PTSS (partner effect). However, the patients' perceived family resilience was not significantly related to their or the caregivers' PTSS. CONCLUSIONS: The primary caregivers' perceived family resilience had both actor and partner effects on patient/caregiver PTSS within the first year of breast cancer diagnosis. Family-based interventions should be designed to enhance family resilience to decrease PTSS within families dealing with cancer patients. Supportive care should focus on the primary family caregivers within the first year of breast cancer diagnosis.

A multiple closed-loops robotic calibration for accurate surgical puncture.

BACKGROUND: Robotic puncture system increasingly demands stringent standard in target location accuracy. The positional and orientational transformation relationships among all components of the system are supposed to be calibrated and identified preoperatively. AIMS: The target location performance is directly determined by the calibration result. Therefore, a multiple closed-loops calibration approach is proposed to achieve high-level calibration accuracy in robotic puncture system. MATERIALS & METHODS: This method takes as input the three-dimensional position information of the retro-reflective markers mounted on the surgical tool, which is detected by the optical tracking system in real time during robotic movement. There is less complicated mathematical derivation and calculation in the presented algorithm by applying the closed-loop principle. RESULTS: Experimental results validate that it can achieve accurate robotic target location with less input data and computation-cost, satisfying the clinical puncture requirements. DISCUSSION: The spatial calibration between robotic arm and optical tracking system efficiently realised by the presented approach present an alternative which can be safely applied to the robotic puncture system for accurate insertion. CONCLUSION: Overall, a multiple closed-loops calibration approach is proposed in this work, which may increase surgical efficiency.

Artificial intelligence assisted display in thoracic surgery: development and possibilities.

In this golden age of rapid development of artificial intelligence (AI), researchers and surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The popularity of low-dose computed tomography (LDCT) and the improvement of the video-assisted thoracoscopic surgery (VATS) not only bring opportunities for thoracic surgery but also bring challenges on the way forward. Preoperatively localizing lung nodules precisely, intraoperatively identifying anatomical structures accurately, and avoiding complications requires a visual display of individuals' specific anatomy for surgical simulation and assistance. With the advance of AI-assisted display technologies, including 3D reconstruction/3D printing, virtual reality (VR), augmented reality (AR), and mixed reality (MR), computer tomography (CT) imaging in thoracic surgery has been fully utilized for transforming 2D images to 3D model, which facilitates surgical teaching, planning, and simulation. AI-assisted display based on surgical videos is a new surgical application, which is still in its infancy. Notably, it has potential applications in thoracic surgery education, surgical quality evaluation, intraoperative assistance, and postoperative analysis. In this review, we illustrated the current AI-assisted display applications based on CT in thoracic surgery; focused on the emerging AI applications in thoracic surgery based on surgical videos by reviewing its relevant researches in other surgical fields and anticipate its potential development in thoracic surgery.