Citations

This systematic review evaluates current computer vision models and their applications in laparoscopic cholecystectomy. Using PRISMA guidelines, we analyzed studies reporting on primary computer vision model applications in laparoscopic cholecystectomy, from inception to March 2026. A total of 85 studies were included: workflow analysis (n = 26), anatomy recognition and segmentation (n = 14), safety assessment (n = 14), instrument detection (n = 12), tissue/image characterization (n = 7), event detection and scene understanding (n = 6), and surgical performance assessment (n = 6). The performance of the computer vision models in the studies was heterogeneous, with accuracy, F1-score, mean average precision, Dice coefficient, mean absolute error, and area under the receiver operating characteristic curve being commonly reported. Despite strong model performances in several studies, implementation in the clinical setting remains a challenge. We highlight the need for standardized model performance reporting, broader external and multi-institutional validation of models, and coordinated frameworks for safe and effective implementation of computer vision models in laparoscopic cholecystectomy.

Abstract: Background Artificial intelligence (AI)-assisted colonoscopy reduces costs in colorectal cancer screening, but its incremental value relative to non-AI resect-and-discard (RD) strategies for diminutive polyps (≤ 5 mm) remain unclear. We aimed to compare the clinical, economic, and environmental outcomes of AI-assisted and non-AI RD strategies with standard of care (SoC). Methods In our Markov model simulating 6 million individuals aged ≥45 years undergoing screening colonoscopy, 4 strategies were evaluated: SoC, non-AI RD, CADe-assisted RD (CADe-RD), and CADe+CADx-assisted RD (CADe+CADx-RD). The primary outcome was total quality-adjusted life years (QALYs), with secondary outcomes including cost and carbon dioxide (CO₂) emissions. Results Non-AI RD achieved the lowest total cost ($36.6B) and carbon emissions (0.90 million kg CO₂), while yielding the highest QALYs (51.37 million), although differences in QALYs across strategies were small (<0.01%). CADe-RD and CADe+CADx-RD remained cost-saving relative to SoC ($36.9B and $37.4B vs $37.8B, respectively) but were associated with higher costs than non-AI RD. CADe modestly increased adenoma detection without improving QALYs, while the addition of CADx increased costs and emissions and resulted in slightly lower QALYs. Sensitivity analysis showed that CADe+CADx-RD matched or exceeded CADe-RD QALYs only within a limited region of high diagnostic performance. At currently reported CADx performance levels, CADe+CADx-RD yielded lower QALYs and higher costs than CADe-RD. CADe+CADx-RD achieved non-inferiority in QALYs relative to CADe-RD only within a limited region of high diagnostic performance. Conclusions In our non-deterministic model, non-AI RD provided the optimal balance of clinical benefit, cost savings, and lowest environmental impact.

Summary Background Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients’ CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell’s concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies. Funding National Institute of Health Research in context Evidence before this study We searched PubMed and Google Scholar on July 7, 2026 for English language studies using the terms (“mortality prediction” OR “death prediction”) AND (“integrated body composition analysis” OR “body composition metrics integration”) AND (“predictive model” OR “model development”), and identified 41 publications describing predictive models incorporating body composition metrics. Recent advances in artificial intelligence (AI) have enabled increasingly granular quantification of body composition from CT, with multiple tissues and tissue-specific metrics showing independent prognostic value for all-cause mortality (ACM) prediction. However, reported associations of body composition metrics with mortality risk were highly inconsistent or even contradictory across studies. This heterogeneity likely reflected the differences in study design or the inherent complexity of body composition analysis, such as the interaction among variables derived from multiple tissues. Thus, it is difficult to interpret body composition results reliably in clinical practice. We hypothesized that integrating multiple body composition metrics could achieve comprehensive and reliable prediction of mortality across patient populations. Added value of this study In this large international, multicenter longitudinal cohort study of 28509 consecutive patients with suspected or known coronary artery disease (CAD) undergoing myocardial perfusion imaging across 12 sites in four countries, we developed and externally validated an artificial intelligence-based body composition index (BCI) for all-cause mortality prediction. The BCI integrated chest CT-derived metrics of six body composition tissue compartments – bone, skeletal muscle (SM), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT), together with demographics (age, sex, BMI) and CT-derived coronary artery calcium (CAC) score, all derived automatically from routine low-dose attenuation correction CT scans without additional imaging or laboratory tests. In an external validation cohort of 7028 patients, the BCI achieved robust discrimination for mortality prediction, substantially outperforming a baseline model using demographics and calcium score alone, and exceeded the discriminative performance of prior body composition models. Performance was consistent across subgroups defined by sex, age, body mass index, cardiometabolic risk factors, imaging modality, and acquisition protocol, supporting generalizability across clinical settings. The BCI demonstrated excellent calibration and net clinical benefit. Explainability analysis identified visceral adipose tissue attenuation and skeletal muscle volume as the dominant contributors to mortality risk, offering tissue-specific therapeutic targets that complement generic weight-based metrics. Model-based simulations suggested that improvements in these specific compartments could reduce estimated mortality risk more effectively than equivalent changes in BMI alone, pointing toward precision therapeutic targets beyond weight reduction. Our study improved over previous literatures on body composition-based predictive models in one or more of the following ways: (1) we considered six types of body composition tissues while existing studies considered only three or less tissues, thus significantly higher granularity and improved ability to capture refined tissue interactions; (2) our model was rigorously validated in external sites, showing cross-site generalizability while most previous work lacked external validation; (3) our model achieved significant prediction performance improvement over previous body composition predictive models, offering better clinical outcomes prediction and facilitating the suitability for widespread clinical adoption; (4) our study was conducted with a large multi-center cohort across multiple countries, achieving statistical power and demonstrating reliability towards patients characteristics and imaging acquisition protocols while previous studies involved small single-center cohorts; (5) our model required only CT scans and basic demographics which were widely available in clinical routine while the existing models required dedicated laboratory tests or specialized imaging modalities, enhancing the applicability in routine clinical practice. Implications of all the available evidence BCI offers an explainable and clinically deployable tool for improved prediction of mortality risk from existing chest CT scans. Its use could inform updates to existing risk models, guide precision preventive strategies, and support development of therapeutics targeting specific body composition phenotypes.

Abstract Introduction Patients with metastatic spine disease (MSD) present with varying degrees of systemic illness and frailty. Given the palliative intent of surgery and radiation treatment for MSD, treatment risks must be judiciously weighed against potential benefits. We sought to examine the impact of peroperative nutritional status as measured by the PG-SGA on survival in patients undergoing surgery and radiation for MSD. Methods This study involved an international, multicenter prospective, observational registry of patients with MSD: AO Knowledge Forum Tumor’s Metastatic Tumor Research and Outcomes Network (MTRON). Variables including baseline demographics, oncologic data, adverse events (medical adverse events and surgical complications), length of stay, and nutritional status were prospectively collected. The Patient-Generated Subjective Global Assessment (PG-SGA) was used to evaluate nutritional status. Results A total of 589 patients were included in this study; 469 (80%) of whom underwent surgery and radiation and 120 (20%) of whom underwent radiation alone. The majority of patients (362, 61%) were classified as well nourished, however, 68 patients (12%) were classified as severely malnourished. Among patients that underwent surgery and radiation, the median overall survival of well nourished patients was 491 days compared to 117 days in the severely malnourished patients (p &lt; 0.001). A multivariate analysis demonstrated severe malnourishment and ECOG performance status of 3 or 4 were associated with worse overall survival. Conclusion Malnutrition as measured by the PG-SGA demonstrated to be significantly and independently associated with perioperative survival in patients with MSD. Further studies that investigate optimal pre-treatment optimization for patients with MSD in order to mitigate morbidity and mortality risk are essential.

Abstract Introduction As the incidence of metastatic spine disease (MSD) increases, treatments to manage refractory pain, pathologic vertebral body fractures, and spinal cord compression are becoming increasingly common. Given the palliative nature of surgical intervention used to achieve tumor control and thus mitigate loss of neurologic function and compromised ambulatory function, it is essential to mitigate the risk of such procedures. Methods The AO Spine Knowledge Forum Tumor’s Metastatic Tumor Research and Outcomes Network (MTRON), an international, prospective registry for patients with spinal metastases, was used for this query. Prospective collection included baseline health and demographic information, oncologic data (including Spinal Instability Neoplastic Score (SINS) score and Bilksy epidural compression classification), and baseline functional status (preoperative American Spinal Injury Association (ASIA) and ECOG scores). Health related quality of life (HRQoL) data included Spine Oncology Study Group Outcomes Questionnaire (SOSGOQ2.0) and Euro-QoL measures. Surgery related adverse events (AE’s) were collected using the Spine Adverse Events Severity (SAVES V2) framework. Kaplan-Meir curves were used to assess survival. Results The cohort consisted of 1267 patients, 701 male (55.3%). A total of 245 (19.3%) experienced at least one postoperative AE, the majority of which were considered minor (grade 3 or less). Among the most common were deep wound infections (3.2%), wound dehiscence (2.5%), and urinary infections (2.4%). A multivariate analysis demonstrated that age, smoking status, ECOG performance, prior radiotherapy, presence of multiple metastases at other sites, and multiple spine metastases were significant risk factors for a postoperative AE. Postoperative AE’s (p = 0.049) and major postoperative AE’s (p = 0.0038) were found to be related to survival as well as a significant change in HRQoL scores (p &lt; 0.001). Conclusion Postoperative AE’s are extremely relevant to the care of patients with MSD and impact both HRQoL and survival.

Abstract Introduction: Erythromelalgia is a descriptive term for burning pain and erythema in distal extremities, often worsened by heat and improved by cold. Inherited erythromelalgia has been primarily linked to gain-of-function variants in SCN9A , encoding voltage-gated sodium channel NaV1.7. However, approximately 65% to 85% of patients with erythromelalgia do not have pathogenic SCN9A variants. Objectives: The objective of this study was to uncover and assess gene variants potentially associated with pediatric-onset erythromelalgia. Methods: With IRB approval and informed consent, probands and families with erythromelalgia underwent next-generation sequencing. A list of genes of interest was produced based on Mendelian inheritance models. Selected gene candidates were assessed using the Sequence Kernel Association Test-Optimal (SKAT-O). Results: Sixty-two probands with erythromelalgia and their relatives were included in Mendelian analysis, which identified variants in PR domain zinc finger protein 12 ( PRDM12 ) and dihydropyrimidinase-like protein 2 ( DPYSL2 ). In a targeted 12-gene rare-variant set analysis using SKAT-O, zinc finger homeobox protein 2 ( ZFHX2 ) showed evidence of association ( P = 6.9 × 10 −4 ), surpassing Bonferroni correction for 12 tests (α = 4.17 × 10 −3 ), whereas KIF1B showed only a nominal association signal ( P = 0.03) that did not survive multiple-testing correction. Conclusion: Genes associated with both increased and decreased pain sensitivity are of considerable interest for elucidating pain mechanisms and analgesic development. As rare variants in PRDM12 and DPYSL2 were identified in a pediatric erythromelalgia cohort and a gene-based rare-variant association signal for ZFHX2 was identified, replication and functional validation are needed.

Objective: To determine whether sleep apnea–specific hypoxic burden (SASHB) is associated with self-reported hypertension beyond conventional obstructive sleep apnea metrics in rail workers. Methods: Cross-sectional study of 84 Argentine rail workers undergoing home sleep apnea testing. Associations between SASHB and hypertension were evaluated using adjusted logistic regression. Drowsy driving was examined as an unadjusted exploratory secondary outcome. Results: Higher SASHB was independently associated with hypertension in the fully adjusted model (OR per SD 2.42; 95% CI 1.24–5.29; p=0.015). Higher SASHB was also associated with more severe self-reported drowsy driving in unadjusted exploratory ordinal analyses (OR per SD 1.78; 95% CI 1.09–3.28; p=0.035). Conclusions: SASHB may provide complementary information beyond conventional OSA metrics for cardiovascular risk assessment in occupational populations.

Long-established prostate cancer cell lines provide limited insight into how contemporary early prostate cancer evolves into drug-resistant and neuroendocrine-like refractory disease. Patient-derived three-dimensional (3D) ex vivo models may better preserve this transition. Here, we found that 8 of 10 magnetic resonance imaging-guided biopsy specimens from patients with early-stage prostate cancer generated sustained 3D tumor spheroid cultures. After 12 weeks of enzalutamide selection, only one patient-derived culture acquired a resistant phenotype with treatment-emergent neuroendocrine prostate cancer (t-NEPC)-like features, including increased chromogranin A (CgA) and synaptophysin (SYP); reduced androgen receptor (AR), prostate-specific antigen (PSA), and prostate-specific membrane antigen (PSMA); and conversion from compact spheroids into irregular resistant aggregates. During this transition, WD repeat and SOCS box-containing protein 1 (WSB1) increased, whereas PSMA progressively decreased. WSB1 silencing restored PSMA, AR, and PSA expression and reduced neuroendocrine-associated features. A similar WSB1 dependency was observed in enzalutamide-resistant LNCaP cells, the castration-resistant prostate cancer model 22Rv1, and the neuroendocrine/small-cell prostate cancer model NCI-H660. Mechanistically, WSB1 functioned as a SOCS box-dependent E3 ubiquitin ligase adaptor that promoted PSMA ubiquitination and degradation. SOCS box deletion or T380A mutation impaired this process, while Aurora kinase A (AURKA) inhibition reduced WSB1-dependent PSMA ubiquitination. WSB1 depletion, AURKA inhibition with alisertib, and combined AURKA inhibition with EZH2 suppression reduced resistant aggregate growth and increased apoptosis-associated markers in patient-derived enzalutamide-resistant neuroendocrine-like spheroids and related models. These findings nominate the AURKA–WSB1–PSMA axis as a therapeutic vulnerability in refractory prostate cancer.