According to the validation results, SOMNUM exhibited high sensitivity and specificity in interpreting apnea and hypopnea from polysomnography (PSG) across all groups of sleep-disordered breathing patients, with excellent predictive performance in mild, moderate, and severe sleep apnea cases.<\/p>\n
The study, titled “A Deep Learning Algorithm Model to Automatically Score and Grade Obstructive Sleep Apnea in Adult Polysomnography,” was published in the latest issue of the SCIE-level international journal Digital Health<\/i>\u00a0 (Volume 10: 1\u201313).<\/p>\n
The clinical study was conducted by Prof. Choi JiHo<\/span>, Head of the Center for Sleep Medicine at SoonChunHyang University Hospital Bucheon, and Prof. Park MarnJoon of the Department of Otolaryngology at Inha University Hospital. It involved 1,000 adults diagnosed with various levels of sleep-disordered breathing through polysomnography, including simple snoring and mild, moderate and severe sleep apnea.<\/p>\n Comparing data interpreted by the AI-based SOMNUM with expert readings of polysomnography, the study showed high sensitivity (95% CI: 98.06\u201398.51) and specificity (95% CI: 95.46\u201397.79) for detecting apnea and hypopnea across all sleep-disordered breathing groups.<\/p>\n SOMNUM also demonstrated excellent predictive accuracy for sleep apnea across all severity levels. The AUC (area under the ROC curve) scores for disease prediction in mild, moderate, and severe groups were 0.9402, 0.9388 and 0.9442, respectively, with no significant differences among the groups.<\/p>\n Sean Ha<\/span> (Tae Kyoung\u00a0Ha), the president of\u00a0HoneyNaps\u00a0USA Inc.,\u00a0“We are pleased that the clinical efficacy and efficiency of our sleep medical\u00a0AI solutions are being validated through a reputable journal as well as through R&D and clinical trials, allowing steady adoption in medical settings. We will continue our research and development efforts and publish diverse study outcomes in global journals to validate the clinical value of HoneyNaps’ medical AI technology.”<\/p>\n For further information, please contact: \u00a0<\/p>\n <\/p>\n – Published clinical study on sleep apnea patients in the prestigious journal Digital Health<\/i><\/b><\/p>\n – Clinical research on HoneyNaps SOMNUM validates its clinical efficacy<\/b><\/p>\n BOSTON<\/span><\/span>, Nov. 11, 2024<\/span><\/span> \/PRNewswire\/ — Medical AI company HoneyNaps has published a paper demonstrating the clinical value and efficacy of its sleep disorder diagnostic algorithm, SOMNUM, in a leading international journal.<\/p>\n According to the validation results, SOMNUM exhibited high sensitivity and specificity in interpreting apnea and hypopnea from polysomnography (PSG) across all groups of sleep-disordered breathing patients, with excellent predictive performance in mild, moderate, and severe sleep apnea cases.<\/p>\n The study, titled “A Deep Learning Algorithm Model to Automatically Score and Grade Obstructive Sleep Apnea in Adult Polysomnography,” was published in the latest issue of the SCIE-level international journal Digital Health<\/i>\u00a0 (Volume 10: 1\u201313).<\/p>\n The clinical study was conducted by Prof. Choi JiHo<\/span>, Head of the Center for Sleep Medicine at SoonChunHyang University Hospital Bucheon, and Prof. Park MarnJoon of the Department of Otolaryngology at Inha University Hospital. It involved 1,000 adults diagnosed with various levels of sleep-disordered breathing through polysomnography, including simple snoring and mild, moderate and severe sleep apnea.<\/p>\n Comparing data interpreted by the AI-based SOMNUM with expert readings of polysomnography, the study showed high sensitivity (95% CI: 98.06\u201398.51) and specificity (95% CI: 95.46\u201397.79) for detecting apnea and hypopnea across all sleep-disordered breathing groups.<\/p>\n SOMNUM also demonstrated excellent predictive accuracy for sleep apnea across all severity levels. The AUC (area under the ROC curve) scores for disease prediction in mild, moderate, and severe groups were 0.9402, 0.9388 and 0.9442, respectively, with no significant differences among the groups.<\/p>\n Sean Ha<\/span> (Tae Kyoung\u00a0Ha), the president of\u00a0HoneyNaps\u00a0USA Inc.,\u00a0“We are pleased that the clinical efficacy and efficiency of our sleep medical\u00a0AI solutions are being validated through a reputable journal as well as through R&D and clinical trials, allowing steady adoption in medical settings. We will continue our research and development efforts and publish diverse study outcomes in global journals to validate the clinical value of HoneyNaps’ medical AI technology.”<\/p>\n For further information, please contact:
HoneyNaps USA<\/span>, Inc.
Christine Kwon<\/span> \/ Managing Director
Email: sleep@honeynaps.com<\/a>
Address: #517, SPACES, 361 Newbury Street, Boston, MA<\/span>, 02115
Website: www.honeynaps.com<\/a>\u00a0<\/p>\n![\"[Area](\"https:\/\/mma.prnasia.com\/media2\/2553689\/Area_under_the_receiver_operating_characteristic__ROC__curve_of_a_deep_learning_algorithm_model_for.jpg?p=medium600\" "\\"[Area")
<\/a>
[Area under the receiver operating characteristic (ROC) curve of a deep learning algorithm model for predicting obstructive sleep apnea (OSA).]<\/span><\/p>\n<\/div>\n
HoneyNaps USA<\/span>, Inc.
Christine Kwon<\/span> \/ Managing Director
Email: sleep@honeynaps.com<\/a>
Address: #517, SPACES, 361 Newbury Street, Boston, MA<\/span>, 02115
Website: www.honeynaps.com<\/a>\u00a0<\/p>\n