The University of Central Florida College of Medicine is an academic college of the University of Central Florida located in Orlando, Florida, United States. The VP of Health Affairs and dean of the college is Deborah C. German, M.D. The college consists of a public medical school and the Burnett School of Biomedical Sciences located on the UCF Health Sciences campus in Lake Nona Medical City. The UCF Lake Nona Medical Center is set to open in early 2021. In December 2018, UCF acquired the former Sanford Burnham Prebys Institute facility nearby which will house the new UCF Lake Nona Cancer Center.
Artificial intelligence (AI) for surgical workflow analysis often fails to generalize because surgical actions lack a standardized, fine-grained representation. Gesture-level “tokenization” of surgery, capturing instrument–tissue interactions as the smallest intentional functional units, offers greater technical specificity than phase- or step-level labels and has demonstrated associations with proficiency and clinical outcomes. However, the field remains fragmented by heterogeneous gesture terminology, limiting dataset interoperability and model reproducibility. We conducted a SAGES-led, accelerated Delphi consensus process to establish a standardized surgical gesture taxonomy. Starting with 270 literature-derived gesture terms, we employed a novel hybrid pipeline combining large language model (LLM)-assisted semantic clustering with multi-round expert review. The process involved two Delphi surveys (open-ended, then structured agreement) with a predefined ≥ 80
Frozen embryo transfers (FET) have become an integral element of assisted reproduction. There is a paucity of data on the impact of different gonadotropins (Gn) used during the index fresh cycle on FET outcome. Since the source of LH activity in the index cycle may affect subsequent FET outcomes, we compared FET cumulative live-birth rates (cLBR) based on the type of gonadotropin used. Retrospective analysis was used to evaluate FET outcome based on the type of Gn used in the index fresh cycle: rFSH (n = 775), hpHMG + / − rFSH (n = 306), or rFSH/rLH (2:1 ratio; n = 232). Comparisons were initially performed between rFSH alone vs mixed protocol groups for all cycles and in only those using a mixed protocol (rLH vs hCG). Generalized linear models, linear mixed-effects models, or multinomial regression analysis was used. FET cLBR was significantly higher in those using rFSH alone vs mixed protocol (45.8
Opportunities to integrate didactic anatomy knowledge with clinically relevant context are often limited. Our study presents a teaching approach to femoral triangle anatomy combining dissection, computed tomography, and a hands-on activity accessing the femoral artery on anatomical donors. The objective was threefold: to teach a clinically relevant skill, evaluate student perceptions of the teaching methods, and assess the effectiveness of this approach in teaching femoral triangle anatomy. All students successfully injected the femoral artery, reported positive perceptions of the demonstration, and showed increased anatomy knowledge. This multimodal framework demonstrates a feasible approach for integrating clinically relevant skills into anatomy education.
Syndromic craniosynostosis is characterized by premature fusion of one or more cranial sutures, often in association with multisystem anomalies affecting the airway, cardiovascular, musculoskeletal, and neurodevelopmental systems. Variants in genes such as TWIST1 contribute to phenotypic heterogeneity and may influence surgical timing, risk stratification, and long-term craniofacial planning. We present a severe syndromic craniosynostosis phenotype associated with a previously undescribed TWIST1 variant and discuss perioperative considerations of staged cranial vault reconstruction. We report a female infant with craniofacial dysmorphism and multisuture craniosynostosis with complete fusion of the bilateral coronal sutures and widening of the sagittal and metopic sutures. Her phenotype included hypertelorism, frontal bossing, exophthalmos, micrognathia, microtia with aural atresia, cleft palate, and limb anomalies. Additional comorbidities included cardiovascular, respiratory, and feeding abnormalities. Genetic testing revealed a novel TWIST1 missense variant (c.423C > G; p.Asp141Glu), not previously reported in population databases or associated with TWIST1-related disease. Due to progressive dysmorphology and worsening orbital proptosis, early strip craniectomy was performed to permit brain-driven anterior vault expansion. At 8.8 months, PVDO with virtual surgical planning was performed to improve intracranial volume and cranial morphology. The postoperative course was complicated by respiratory failure, cardiac arrest, intracranial abscess, and pseudomeningocele requiring surgical management. This case highlights the expanding genotypic and phenotypic variability associated with TWIST1 alterations. It emphasizes the need for ongoing genetic investigation to delineate pathogenic variants, improve prognostication, and refine surgical planning in complex craniosynostosis.
BACKGROUND:Fibroepithelial breast lesions, including fibroadenomas and phyllodes tumors (PTs), can be difficult to classify on needle biopsy. Misclassification may result in unnecessary excisions of benign fibroadenomas or delays and repeat operations for borderline/malignant PTs. Artificial Intelligence for Fibroepithelial Lesion Evaluation and Extrication Technology (AI-FLEET) is a multi-stage program designed to improve diagnostic accuracy and reduce inconclusive preoperative assessments by integrating radiologic, pathologic, and clinical data. PATIENTS AND METHODS:In this first phase, we retrospectively analyzed patients with histologically confirmed PTs. Borderline and malignant PTs were grouped together owing to similarities in margin management and the limited number of cases. Models were trained to distinguish benign from borderline/malignant PTs using ultrasound images and clinical variables (age, body mass index (BMI), race/ethnicity, menopausal status, echogenicity, and tumor size). Multiple convolutional and attention-based encoders were evaluated using subject-stratified five-fold cross-validation. RESULTS:The cohort included 81 patients (65 benign, 16 borderline/malignant PTs) with 1638 ultrasound images. The multimodal ConvNeXt model achieved an accuracy of 0.91 (AUC 0.94), while the multimodal ResNet18 achieved an accuracy of 0.92 (AUC 0.94). Other multimodal architectures showed lower performance. Ultrasound-only and clinical-only models reached AUCs of 0.89 and 0.78, respectively. Saliency analyses identified intratumoral heterogeneity as an important predictive feature. CONCLUSIONS:Multimodal deep learning models combining ultrasound and clinical factors achieved high accuracy in differentiating benign from borderline/malignant PTs, demonstrating the feasibility of AI-assisted assessment of fibroepithelial lesions. Phase II will expand this work by incorporating histopathology and fibroadenoma cases to further enhance radiologic-pathologic integration.
