Background and Objectives: Inconsistencies in nasal nitric oxide (nNO) values, due to anatomical variations and comorbidities, challenge the accurate assessment of upper airway inflammation severity. We hypothesized that changes in nNO levels following treatment for chronic rhinitis would be consistent and provide relative value. This study aimed to evaluate the correlation between changes in nNO levels and symptomatic improvements following treatment for chronic rhinitis. Methods: This prospective observational study included 46 participants diagnosed with chronic rhinitis between December 2021 and November 2023. nNO measurements, evaluations of four nasal and two ocular symptoms, and quality of life questionnaires were conducted at baseline and after one month of treatment. Baseline laboratory tests included serum total immunoglobulin E levels, blood eosinophil percentages, and skin prick tests. Results: The Total Nasal Symptom Score (TNSS), TNSS with ocular symptoms (TNSS eye), and Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) scores significantly decreased following treatment (all p<0.001). nNO levels also decreased significantly after treatment (p=0.036). Moreover, changes in nNO were significantly correlated with changes in TNSS, TNSS eye, and RQLQ scores (p=0.047, r=0.294; p=0.021, r=0.340; and p=0.004, r=0.419, respectively). Conclusion In patients with chronic rhinitis, changes in TNSS, TNSS eye, and RQLQ scores were correlated with changes in nNO levels after treatment. nNO may serve as a potential objective evaluation tool for chronic rhinitis, particularly in patients who have difficulty reporting symptoms.

Background and Objectives: Inconsistencies in nasal nitric oxide (nNO) values, due to anatomical variations and comorbidities, challenge the accurate assessment of upper airway inflammation severity. We hypothesized that changes in nNO levels following treatment for chronic rhinitis would be consistent and provide relative value. This study aimed to evaluate the correlation between changes in nNO levels and symptomatic improvements following treatment for chronic rhinitis. Methods: This prospective observational study included 46 participants diagnosed with chronic rhinitis between December 2021 and November 2023. nNO measurements, evaluations of four nasal and two ocular symptoms, and quality of life questionnaires were conducted at baseline and after one month of treatment. Baseline laboratory tests included serum total immunoglobulin E levels, blood eosinophil percentages, and skin prick tests. Results: The Total Nasal Symptom Score (TNSS), TNSS with ocular symptoms (TNSS eye), and Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) scores significantly decreased following treatment (all p<0.001). nNO levels also decreased significantly after treatment (p=0.036). Moreover, changes in nNO were significantly correlated with changes in TNSS, TNSS eye, and RQLQ scores (p=0.047, r=0.294; p=0.021, r=0.340; and p=0.004, r=0.419, respectively). Conclusion In patients with chronic rhinitis, changes in TNSS, TNSS eye, and RQLQ scores were correlated with changes in nNO levels after treatment. nNO may serve as a potential objective evaluation tool for chronic rhinitis, particularly in patients who have difficulty reporting symptoms.

Background and Objectives: Inconsistencies in nasal nitric oxide (nNO) values, due to anatomical variations and comorbidities, challenge the accurate assessment of upper airway inflammation severity. We hypothesized that changes in nNO levels following treatment for chronic rhinitis would be consistent and provide relative value. This study aimed to evaluate the correlation between changes in nNO levels and symptomatic improvements following treatment for chronic rhinitis. Methods: This prospective observational study included 46 participants diagnosed with chronic rhinitis between December 2021 and November 2023. nNO measurements, evaluations of four nasal and two ocular symptoms, and quality of life questionnaires were conducted at baseline and after one month of treatment. Baseline laboratory tests included serum total immunoglobulin E levels, blood eosinophil percentages, and skin prick tests. Results: The Total Nasal Symptom Score (TNSS), TNSS with ocular symptoms (TNSS eye), and Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) scores significantly decreased following treatment (all p<0.001). nNO levels also decreased significantly after treatment (p=0.036). Moreover, changes in nNO were significantly correlated with changes in TNSS, TNSS eye, and RQLQ scores (p=0.047, r=0.294; p=0.021, r=0.340; and p=0.004, r=0.419, respectively). Conclusion In patients with chronic rhinitis, changes in TNSS, TNSS eye, and RQLQ scores were correlated with changes in nNO levels after treatment. nNO may serve as a potential objective evaluation tool for chronic rhinitis, particularly in patients who have difficulty reporting symptoms.

Purpose: Cardiac radioablation is a novel, non-invasive treatment for ventricular tachycardia (VT), involving a single fractional stereotactic ablative body radiotherapy (SABR) session with a prescribed dose of 25 Gy. This complex procedure requires a detailed workflow and stringent dose constraints compared to conventional radiation therapy. This study aims to establish a consistent institutional workflow for single-fraction cardiac VT-SABR, emphasizing robust plan evaluation and quality assurance. Materials and Methods: The study developed a consistent institutional workflow for VT-SABR, including computed tomography (CT) simulation, target volume definition, treatment planning, robust plan evaluation, quality assurance, and image-guided strategy. The workflow was implemented for two patients with cardiac arrhythmia. Accurate target volume definition using planning CT images and electronic anatomical mapping was critical. A four-dimensional (4D) cone-beam CT (CBCT) and breath-hold electrocardiographic gated CT images reliably detected target motion. Results: The resulting plans exhibited a conformity index greater than 0.7 and a gradient index around G4.0. Dose constraints for the planning target volume (PTV) aimed for 95% or higher PTV dose coverage, with a maximum dose of 200% or lower. However, one case did not meet the PTV dose coverage due to the proximity of the PTV to gastrointestinal organs. Plans adhered to dose constraints for organs at risk near the heart, but meeting constraints for specific cardiac sub-structures was challenging and dependent on PTV location. Conclusion The plans demonstrated robustness against respiratory motion and patient positional uncertainty through a robust evaluation function. The 4D and intra-fractional CBCT were effective in verifying target motion and setup stability.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Purpose: Cardiac radioablation is a novel, non-invasive treatment for ventricular tachycardia (VT), involving a single fractional stereotactic ablative body radiotherapy (SABR) session with a prescribed dose of 25 Gy. This complex procedure requires a detailed workflow and stringent dose constraints compared to conventional radiation therapy. This study aims to establish a consistent institutional workflow for single-fraction cardiac VT-SABR, emphasizing robust plan evaluation and quality assurance. Materials and Methods: The study developed a consistent institutional workflow for VT-SABR, including computed tomography (CT) simulation, target volume definition, treatment planning, robust plan evaluation, quality assurance, and image-guided strategy. The workflow was implemented for two patients with cardiac arrhythmia. Accurate target volume definition using planning CT images and electronic anatomical mapping was critical. A four-dimensional (4D) cone-beam CT (CBCT) and breath-hold electrocardiographic gated CT images reliably detected target motion. Results: The resulting plans exhibited a conformity index greater than 0.7 and a gradient index around G4.0. Dose constraints for the planning target volume (PTV) aimed for 95% or higher PTV dose coverage, with a maximum dose of 200% or lower. However, one case did not meet the PTV dose coverage due to the proximity of the PTV to gastrointestinal organs. Plans adhered to dose constraints for organs at risk near the heart, but meeting constraints for specific cardiac sub-structures was challenging and dependent on PTV location. Conclusion The plans demonstrated robustness against respiratory motion and patient positional uncertainty through a robust evaluation function. The 4D and intra-fractional CBCT were effective in verifying target motion and setup stability.

Purpose: Cardiac radioablation is a novel, non-invasive treatment for ventricular tachycardia (VT), involving a single fractional stereotactic ablative body radiotherapy (SABR) session with a prescribed dose of 25 Gy. This complex procedure requires a detailed workflow and stringent dose constraints compared to conventional radiation therapy. This study aims to establish a consistent institutional workflow for single-fraction cardiac VT-SABR, emphasizing robust plan evaluation and quality assurance. Materials and Methods: The study developed a consistent institutional workflow for VT-SABR, including computed tomography (CT) simulation, target volume definition, treatment planning, robust plan evaluation, quality assurance, and image-guided strategy. The workflow was implemented for two patients with cardiac arrhythmia. Accurate target volume definition using planning CT images and electronic anatomical mapping was critical. A four-dimensional (4D) cone-beam CT (CBCT) and breath-hold electrocardiographic gated CT images reliably detected target motion. Results: The resulting plans exhibited a conformity index greater than 0.7 and a gradient index around G4.0. Dose constraints for the planning target volume (PTV) aimed for 95% or higher PTV dose coverage, with a maximum dose of 200% or lower. However, one case did not meet the PTV dose coverage due to the proximity of the PTV to gastrointestinal organs. Plans adhered to dose constraints for organs at risk near the heart, but meeting constraints for specific cardiac sub-structures was challenging and dependent on PTV location. Conclusion The plans demonstrated robustness against respiratory motion and patient positional uncertainty through a robust evaluation function. The 4D and intra-fractional CBCT were effective in verifying target motion and setup stability.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Purpose: Cardiac radioablation is a novel, non-invasive treatment for ventricular tachycardia (VT), involving a single fractional stereotactic ablative body radiotherapy (SABR) session with a prescribed dose of 25 Gy. This complex procedure requires a detailed workflow and stringent dose constraints compared to conventional radiation therapy. This study aims to establish a consistent institutional workflow for single-fraction cardiac VT-SABR, emphasizing robust plan evaluation and quality assurance. Materials and Methods: The study developed a consistent institutional workflow for VT-SABR, including computed tomography (CT) simulation, target volume definition, treatment planning, robust plan evaluation, quality assurance, and image-guided strategy. The workflow was implemented for two patients with cardiac arrhythmia. Accurate target volume definition using planning CT images and electronic anatomical mapping was critical. A four-dimensional (4D) cone-beam CT (CBCT) and breath-hold electrocardiographic gated CT images reliably detected target motion. Results: The resulting plans exhibited a conformity index greater than 0.7 and a gradient index around G4.0. Dose constraints for the planning target volume (PTV) aimed for 95% or higher PTV dose coverage, with a maximum dose of 200% or lower. However, one case did not meet the PTV dose coverage due to the proximity of the PTV to gastrointestinal organs. Plans adhered to dose constraints for organs at risk near the heart, but meeting constraints for specific cardiac sub-structures was challenging and dependent on PTV location. Conclusion The plans demonstrated robustness against respiratory motion and patient positional uncertainty through a robust evaluation function. The 4D and intra-fractional CBCT were effective in verifying target motion and setup stability.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.