Ann. Q. Tran

Breast carcinoma is the most common primary malignancy to metastasize to the orbit.1 Among breast cancer types known to metastasize to the orbit, invasive lobular carcinoma has a specific predilect...

Lacrimal sac epithelial tumors are rare and often present with subtle symptoms. We report 2 cases managed with combined external dacryocystectomy and endoscopic endonasal resection. Case 1 involved a 59-year-old female with lacrimal sac inverted papilloma extending into the nasolacrimal duct and inferior meatus. Case 2 involved a 79-year-old male with a chronically draining medial canthal wound and progressive induration after prior cyst excision, ultimately diagnosed as lacrimal sac oncocytoma. In both cases, preoperative imaging and clinical features demonstrated nasolacrimal duct involvement and adjacent anatomic distortion, prompting combined surgery for direct visualization and controlled resection of the lacrimal sac and ductal components. The combined approach allowed safe excision despite ductal expansion and adjacent tissue involvement. A small localized recurrence in case 1 at 11 months was successfully managed with in-office resection. These cases support case-selected multidisciplinary external-endonasal surgery for lacrimal sac tumors with nasolacrimal duct involvement.

Background: Periorbital measurements such as margin to reflex distances, palpebral fissure height, and scleral show are critical in diagnosing and managing conditions like ptosis and disorders of the eyelid. However, deployment of automated periorbital measurement algorithms in structured research workflows remains limited by the lack of integrated capture and data management infrastructure. Objective: We developed and evaluated Glorbit, a lightweight, browser-based application for automated periorbital distance measurement using artificial intelligence (AI). The objective was to evaluate end-to-end workflow feasibility of the platform under simulated, operator-run conditions. Methods: The application integrates a DeepLabV3 segmentation model into a modular image processing pipeline with secure, site-specific Google Cloud storage, supporting local preprocessing and cloud upload through Firebase-authenticated logins. The full workflow—metadata entry, facial image capture, segmentation, and upload—was tested. After the session, the participants completed a Likert-style survey. Results: Glorbit successfully ran on all tested platforms, including laptops, tablets, and mobile phones across major browsers. A total of 15 volunteers were enrolled in this study in which the app completed predefined workflow steps in all simulated, operator-run sessions. The segmentation model produced outputs on all images, and the average session duration was 101.7 (SD 17.5) seconds. Simulated experience scores on a 5-point Likert scale were uniformly high. Conclusions: Glorbit is a cross-platform application that supports structured periorbital image capture and automated inference within a unified workflow. In simulated, operator-run testing, the platform demonstrated successful execution of predefined workflow steps across devices. These findings support the technical feasibility of the system as a research-oriented data collection framework and may inform future evaluations in broader research settings.

BACKGROUND Periorbital measurements such as margin to reflex distances, palpebral fissure height, and scleral show are critical in diagnosing and managing conditions like ptosis and disorders of the eyelid. However, deployment of automated periorbital measurement algorithms in structured research workflows remains limited by the lack of integrated capture and data management infrastructure. OBJECTIVE We developed and evaluated Glorbit, a lightweight, browser-based application for automated periorbital distance measurement using artificial intelligence (AI). The objective was to evaluate end-to-end workflow feasibility of the platform under simulated, operator-run conditions. METHODS The application integrates a DeepLabV3 segmentation model into a modular image processing pipeline with secure, site-specific Google Cloud storage, supporting local preprocessing and cloud upload through Firebase-authenticated logins. The full workflow—metadata entry, facial image capture, segmentation, and upload—was tested. After the session, the participants completed a Likert-style survey. RESULTS Glorbit successfully ran on all tested platforms, including laptops, tablets, and mobile phones across major browsers. A total of 15 volunteers were enrolled in this study in which the app completed predefined workflow steps in all simulated, operator-run sessions. The segmentation model produced outputs on all images, and the average session duration was 101.7 (SD 17.5) seconds. Simulated experience scores on a 5-point Likert scale were uniformly high. CONCLUSIONS Glorbit is a cross-platform application that supports structured periorbital image capture and automated inference within a unified workflow. In simulated, operator-run testing, the platform demonstrated successful execution of predefined workflow steps across devices. These findings support the technical feasibility of the system as a research-oriented data collection framework and may inform future evaluations in broader research settings.

The social vulnerability index (SVI) is a community-level scoring system based on US Census tract, designed to assess vulnerability during disasters. Here, we evaluate the relationship of CDC-defined SVI with office visit compliance in an urban oculoplastic surgery clinic. In this single-center retrospective study, age, gender, race/ethnicity, electronic accessibility status, residential address, and number of completed and no-show visits were collected of individuals treated at an oculoplastic clinic from Sept 2020 to Sept 2023. The 2020 Census Tract SVI scores were obtained using the residential addresses for the overall SVI, four thematic SVIs, and 16 social factors' SVIs. A greater SVI (range 0-1) indicates higher social vulnerability. Categorical, numerical, and logistic regression statistical tests were performed to determine factors predictive of unreliable visit attendance, defined as failure to attend >25% of scheduled appointments. The final analysis included 3,287 patients with a mean age of 52 ± 21 years, 61% of whom were female. In the study period, 787 patients (24%) were found to have unreliable attendance at their scheduled appointments. Having an overall SVI of 0.75 or higher was associated with an increased likelihood of unreliable visit attendance (OR 2.7, 95% CI 2.1-3.5, p < 0.001). Additionally, a thematic SVI of 0.75 or higher regarding racial and ethnic minority status resulted in an increased likelihood of unreliable attendance (OR 2.1, 95% CI 1.2-3.4, p = 0.008). This is the first study showing that living in communities with high SVI scores is associated with poor oculoplastic surgery office visit attendance.

To demonstrate the role of Virtual Reality (VR) in orbital surgery as an educational tool for surgical trainees. A single-center prospective study was conducted from February 2021 to April 2023. Pre-operative magnetic resonance imaging and computed tomography scans were used to create patient-specific VR models of the orbit using ImmersiveTouch Software. Accuracy of the models was qualitatively assessed by an attending oculofacial surgeon. Surveys regarding understanding of the surgical plan were distributed to trainees before the VR simulation, after its use, and following surgery. VR models were made for 28 cases, and 52 surveys were completed by residents in ophthalmology, otolaryngology, plastic surgery, and by oculofacial fellows. The VR models for 93% of the cases were rated as accurate by the attending physician. Assigned trainee tasks included measurement of an orbital fracture (n = 1, 1.9%), measurement of orbital tumors (n = 12, 23.1%), and drilling of the orbital bone to simulate an orbital decompression (n = 10, 19.2%). The tumor measurements made by trainees using the VR system were not significantly different than those recorded by the radiologists (p > 0.05). Early trainees (i.e. residents and the first year oculofacial fellow) noted significant improvement in their understanding of the pathology relative to important anatomical landmarks within the orbit after utilizing the VR models prior to surgery (p < 0.001). Patient-specific VR models accurately simulate orbital pathology and may improve trainees' understanding of orbital anatomy early in their careers.