Objectives: Obstructive sleep apnoea (OSA) is a heavily underdiagnosed condition, which can lead to significant multimorbidity. Underdiagnosis is often secondary to limitations in existing diagnostic methods. We conducted a diagnostic accuracy and usability study, to evaluate the efficacy of a novel, low-cost, small, wearable medical device, AcuPebble_SA100, for automated diagnosis of OSA in the home environment.
Settings: Patients were recruited to a standard OSA diagnostic pathway in an UK hospital. They were trained on the use of type-III-cardiorespiratory polygraphy, which they took to use at home. They were also given AcuPebble_SA100; but they were not trained on how to use it.
Participants: 182 consecutive patients had been referred for OSA diagnosis in which 150 successfully completed the study.
Primary Outcome Measures: Efficacy of AcuPebble_SA100 for automated diagnosis of moderate-severe-OSA against cardiorespiratory polygraphy (sensitivity/specificity/likelihood ratios/predictive values) and validation of usability by patients themselves in their home environment.
Results: After returning the systems, two expert clinicians, blinded to AcuPebble_SA100's output, manually scored the cardiorespiratory polygraphy signals to reach a diagnosis. AcuPebble_SA100 generated automated diagnosis corresponding to four, typically followed, diagnostic criteria: Apnoea Hypopnoea Index (AHI) using 3% as criteria for oxygen desaturation; Oxygen Desaturation Index (ODI) for 3% and 4% desaturation criteria and AHI using 4% as desaturation criteria. In all cases, AcuPebble_SA100 matched the experts' diagnosis with positive and negative likelihood ratios over 10 and below 0.1, respectively. Comparing against the current American Academy of Sleep Medicine's AHI-based criteria demonstrated 95.33% accuracy (95% CI (90·62% to 98·10%)), 96.84% specificity (95% CI (91·05% to 99·34%)), 92.73% sensitivity (95% CI (82·41% to 97·98%)), 94.4% positive-predictive value (95% CI (84·78% to 98·11%)) and 95.83% negative-predictive value (95% CI (89·94% to 98·34%)). All patients used AcuPebble_SA100 correctly. Over 97% reported a strong preference for AcuPebble_SA100 over cardiorespiratory polygraphy.
Conclusions: These results validate the efficacy of AcuPebble_SA100 as an automated diagnosis alternative to cardiorespiratory polygraphy; also demonstrating that AcuPebble_SA100 can be used by patients without requiring human training/assistance. This opens the doors for more efficient patient pathways for OSA diagnosis.
Trial Registration Number: NCT03544086; ClinicalTrials.gov.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8693096 | PMC |
http://dx.doi.org/10.1136/bmjopen-2020-046803 | DOI Listing |
J Imaging Inform Med
January 2025
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.
The automated diagnosis of low-resolution and difficult-to-recognize breast ultrasound images through multi-modal fusion holds significant clinical value. However, prevailing fusion methods predominantly rely on image modalities, neglecting the textual pathology information, and only benign and malignant diagnosis of breast tumors is not satisfying for clinical applications. Consequently, this paper proposes a novel multi-modal fusion interactive diagnostic framework, termed the MIC framework, to achieve the multi-label classification of breast cancer, namely benign-malignant classification and breast imaging reporting and data system (BI-RADS) 3, 4a, 4b, 4c, and 5 gradings.
View Article and Find Full Text PDFSci Rep
January 2025
Hyfe, Wilmington, DE, USA.
Background: The ability to passively and continuously monitor coughing for prolonged periods of time would significantly improve cough management and research. To date there is no automated clinically validated cough monitor that can be routinely used in clinical care and research. Here we describe the validation of such an automated cough monitor.
View Article and Find Full Text PDFZhonghua Bing Li Xue Za Zhi
January 2025
Department of Pathology, School of Basic Medical Sciences, Peking University Third Hospital, Beijing100191, China.
Zhonghua Bing Li Xue Za Zhi
January 2025
Department of Pathology, Renmin Hospital of Wuhan University, Wuhan430060, China.
To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant therapy (NAT) for breast cancer. Specimens were collected from 209 breast cancer patients who received NAT at Renmin Hospital of Wuhan University from October 2019 to June 2023. TSR levels in pre-NAT biopsy specimens were automatically computed using a deep learning algorithm and categorized into low stroma (TSR≤30%), intermediate stroma (TSR 30% to ≤60%), and high stroma (TSR>60%) groups.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!