Daksh Chauhan

INTRODUCTION: C-arm fluoroscopy and O-arm navigation are vital tools in modern spine surgeries, but their repeated usage can endanger spine surgeons. While a surgeon’s chest and abdomen are protected by lead aprons, the eyes and extremities generally receive less protection. METHODS: This prospective cohort study includes 65 consecutive spine surgeries performed by a single spine-focused neurosurgeon over a 9-month period. Radiation exposure to the primary surgeon was measured through dosimeters worn over the lead apron, under the lead apron, on surgical loupes, and as a ring on the dominant hand. Differences were assessed with rigorous statistical testing and radiation exposure per surgical case was extrapolated. RESULTS: During the study, the measured radiation exposure over the apron, 176 mrem, was significantly greater than that under the apron, 8 mrem (p = .0020), demonstrating shielding’s protective effect. The surgeon’s dominant hand was exposed to 329 mrem while the eyes were exposed to 152.5 mrem of radiation. Compared to the surgeon’s protected abdominal area, the hands (p = .0002) and eyes (p=.0002) received significantly greater exposure. Calculated exposure per case was 2.8 mrem for the eyes and 5.1 mrem for the hands. It was determined that a spine-focused neurosurgeon operating 400 cases annually will incur a radiation exposure of 60,750 mrem to the hands and 33,900 mrem to the eyes over a 30-year career. CONCLUSIONS: Our study found that spine surgeons encounter significantly more radiation exposure to the eyes and the extremities compared to protected body regions. Lifetime exposure exceeds the annual limits set by the International Commission on Radiological Protection for the extremities (50,000 mrem/yr) and the eyes (15,000 mrem/yr), calling for increased awareness about the dangerous levels of radiation exposure that a spine surgeon incurs over one’s career.

OBJECTIVE Patient-reported outcome measures (PROMs) are the gold standard for assessing postoperative outcomes in spine surgery. However, PROMs are also limited by the inherent subjectivity of self-reported qualitative data. Recent literature has highlighted the utility of patient mobility data streamed from smartphone accelerometers as an objective measure of functional outcomes and complement to traditional PROMs. Still, for activity-based data to supplement existing PROMs, they must be validated against current metrics. In this study, the authors assessed the relationships and concordance between longitudinal smartphone-based mobility data and PROMs. METHODS Patients receiving laminectomy (n = 21) or fusion (n = 10) between 2017 and 2022 were retrospectively included. Activity data (steps-per-day count) recorded in the Apple Health mobile application over a 2-year perioperative window were extracted and subsequently normalized to allow for intersubject comparison. PROMS, includin...

INTRODUCTION: C-arm fluoroscopy and O-arm navigation are vital tools in modern spine surgeries, but their repeated usage can endanger spine surgeons. While a surgeon’s chest and abdomen are protected by lead aprons, the eyes and extremities generally receive less protection. METHODS: This prospective cohort study includes 65 consecutive spine surgeries performed by a single spine-focused neurosurgeon over a 9-month period. Radiation exposure to the primary surgeon was measured through dosimeters worn over the lead apron, under the lead apron, on surgical loupes, and as a ring on the dominant hand. Differences were assessed with rigorous statistical testing and radiation exposure per surgical case was extrapolated. RESULTS: During the study, the measured radiation exposure over the apron, 176 mrem, was significantly greater than that under the apron, 8 mrem (p = .0020), demonstrating shielding’s protective effect. The surgeon’s dominant hand was exposed to 329 mrem while the eyes wer...

Study Design: Prospective cohort study. Summary of Background Data: C-arm fluoroscopy and O-arm navigation are vital tools in modern spine surgeries, but their repeated usage can endanger spine surgeons. Although a surgeon’s chest and abdomen are protected by lead aprons, the eyes and extremities generally receive less protection. Objective: In this study, we compare differences in intraoperative radiation exposure across the protected and unprotected regions of a surgeon’s body. Methods: Sixty-five consecutive spine surgeries were performed by a single spine-focused neurosurgeon over 9 months. Radiation exposure to the primary surgeon was measured through dosimeters worn over the lead apron, under the lead apron, on surgical loupes, and as a ring on the dominant hand. Differences were assessed with rigorous statistical testing and radiation exposure per surgical case was extrapolated. Results: During the study, the measured radiation exposure over the apron, 176 mrem, was significa...

BACKGROUND: The development of accurate machine learning algorithms requires sufficient quantities of diverse data. This poses a challenge in health care because of the sensitive and siloed nature of biomedical information. Decentralized algorithms through federated learning (FL) avoid data aggregation by instead distributing algorithms to the data before centrally updating one global model. OBJECTIVE: To establish a multicenter collaboration and assess the feasibility of using FL to train machine learning models for intracranial hemorrhage (ICH) detection without sharing data between sites. METHODS: Five neurosurgery departments across the United States collaborated to establish a federated network and train a convolutional neural network to detect ICH on computed tomography scans. The global FL model was benchmarked against a standard, centrally trained model using a held-out data set and was compared against locally trained models using site data. RESULTS: A federated network of ...

Background Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development. Methods We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV ...

Background: It is unclear whether artificial intelligence (AI) can provide automatic solutions to measure right ventricular ejection fraction (RVEF), due to the complex RV geometry. Although several deep learning (DL) algorithms are available to quantify RVEF from cardiac magnetic resonance (CMR) images, there has been no systematic comparison of these algorithms, and the prognostic value of these automated measurements is unknown. We aimed to determine whether RVEF measurements made using DL algorithms could be used to risk stratify patients similarly to measurements made by an expert. Methods: We identified from a pre-existing registry 200 patients who underwent CMR. RVEF was determined using 3 fully automated commercial DL algorithms (DL-RVEF) and also by a clinical expert (CLIN-RVEF) using conventional methodology. Each of the DL-RVEF approaches was compared against CLIN-RVEF using linear regression and Bland-Altman analyses. In addition, RVEF values were classified according to...