A novel intermittent negative air pressure device ameliorates obstructive sleep apnea syndrome in adults.

PURPOSE: Patients with obstructive sleep apnea syndrome (OSAS) have difficulties in compliance with continuous positive airway pressure (CPAP) and the treatment outcome is heterogeneous. We proposed a proof-of-concept study of a novel intermittent negative air pressure (iNAP®) device for physicians to apply on patients who have failed or refused to use CPAP. METHODS: The iNAP® device retains the tongue and the soft palate in a forward position to decrease airway obstruction. A full nightly usage with the device was evaluated with polysomnography. Subgrouping by baseline apnea-hypopnea index (AHI) and body mass index (BMI) with different treatment response criteria was applied to characterize the responder group of this novel device. RESULTS: Thirty-five patients were enrolled: age 41.9 ± 12.2 years (mean ± standard deviation), BMI 26.6 ± 4.3 kg/m2, AHI 41.4 ± 24.3 events/h, and oxygen desaturation index (ODI) 40.9 ± 24.4 events/h at baseline. AHI and ODI were significantly decreased (p < 0.001) by the device. Patients with moderate OSAS, with baseline AHI between 15 to 30 events/h, achieved 64% response rate; and non-obese patients, with BMI below 25 kg/m2, achieved 57% response rate, with response rate defined as 50% reduction in AHI from baseline and treated AHI lower than 20. There were minimal side effects reported. CONCLUSIONS: In a proof-of-concept study, the device attained response to treatment as defined, in more than half of the moderate and non-obese OSAS patients, with minimal side effects.

Evaluating Urine Cytology Slide Digitization Efficiency: A Comparative Study Using an Artificial Intelligence-Based Heuristic Scanning Simulation and Multiple Z-Plane Scanning.

INTRODUCTION: Digitizing cytology slides presents challenges because of their three-dimensional features and uneven cell distribution. While multi-Z-plane scan is a prevalent solution, its adoption in clinical digital cytopathology is hindered by prolonged scanning times, increased image file sizes, and the requirement for cytopathologists to review multiple Z-plane images. METHODS: This study presents heuristic scan as a novel solution, using an artificial intelligence (AI)-based approach specifically designed for cytology slide scanning as an alternative to the multi-Z-plane scan. Both the 21 Z-plane scan and the heuristic scan simulation methods were used on 52 urine cytology slides from three distinct cytopreparations (Cytospin, ThinPrep, and BD CytoRich™ [SurePath]), generating whole-slide images (WSIs) via the Leica Aperio AT2 digital scanner. The AI algorithm inferred the WSI from 21 Z-planes to quantitate the total number of suspicious for high-grade urothelial carcinoma or more severe cells (SHGUC+) cells. The heuristic scan simulation calculated the total number of SHGUC+ cells from the 21 Z-plane scan data. Performance metrics including SHGUC+ cell coverage rates (calculated by dividing the number of SHGUC+ cells identified in multiple Z-planes or heuristic scan simulation by the total SHGUC+ cells in the 21 Z-planes for each WSI), scanning time, and file size were analyzed to compare the performance of each scanning method. The heuristic scan's metrics were linearly estimated from the 21 Z-plane scan data. Additionally, AI-aided interpretations of WSIs with scant SHGUC+ cells followed The Paris System guidelines and were compared with original diagnoses. RESULTS: The heuristic scan achieved median SHGUC+ cell coverage rates similar to 5 Z-plane scans across three cytopreparations (0.78-0.91 vs. 0.75-0.88, p = 0.451-0.578). Notably, it substantially reduced both scanning time (137.2-635.0 s vs. 332.6-1,278.8 s, p < 0.05) and image file size (0.51-2.10 GB vs. 1.16-3.10 GB, p < 0.05). Importantly, the heuristic scan yielded higher rates of accurate AI-aided interpretations compared to the single Z-plane scan (62.5% vs. 37.5%). CONCLUSION: We demonstrated that the heuristic scan offers a cost-effective alternative to the conventional multi-Z-plane scan in digital cytopathology. It achieves comparable SHGUC+ cell capture rates while reducing both scanning time and image file size, promising to aid digital urine cytology interpretations with a higher accuracy rate compared to the conventional single (optimal) plane scan. Further studies are needed to assess the integration of this new technology into compatible digital scanners for practical cytology slide scanning.

An Artificial Intelligence-assisted Diagnostic System Improves Upper Urine Tract Cytology Diagnosis.

BACKGROUND/AIM: To evaluate efficacy of the AIxURO system, a deep learning-based artificial intelligence (AI) tool, in enhancing the accuracy and reliability of urine cytology for diagnosing upper urinary tract cancers. MATERIALS AND METHODS: One hundred and eighty-five cytology samples of upper urine tract were collected and categorized according to The Paris System for Reporting Urinary Cytology (TPS), yielding 168 negative for High-Grade Urothelial Carcinoma (NHGUC), 14 atypical urothelial cells (AUC), 2 suspicious for high-grade urothelial carcinoma (SHGUC), and 1 high-grade urothelial carcinoma (HGUC). The AIxURO system, trained on annotated cytology images, was employed to analyze these samples. Independent assessments by a cytotechnologist and a cytopathologist were conducted to validate the initial AIxURO assessment. RESULTS: AIxURO identified discrepancies in 37 of the 185 cases, resulting in a 20% discrepancy rate. The cytotechnologist achieved an accuracy of 85% for NHGUC and 21.4% for AUC, whereas the cytopathologist attained accuracies of 95% for NHGUC and 85.7% for AUC. The cytotechnologist exhibited overcall rates of roughly 15% and undercall rates of greater than 50%, while the cytopathologist showed profoundly lower miscall rates from both undercall and overcall. AIxURO significantly enhanced diagnostic accuracy and consistency, particularly in complex cases involving atypical cells. CONCLUSION: AIxURO can improve the accuracy and reliability of cytology diagnosis for upper urine tract urothelial carcinomas by providing precise detection on atypical urothelial cells and reducing subjectivity in assessments. The integration of AIxURO into clinical practice can significantly ameliorate diagnostic outcomes, highlighting the synergistic potential of AI technology and human expertise in cytology.

Evaluating artificial intelligence-enhanced digital urine cytology for bladder cancer diagnosis.

BACKGROUND: This study evaluated the diagnostic effectiveness of the AIxURO platform, an artificial intelligence-based tool, to support urine cytology for bladder cancer management, which typically requires experienced cytopathologists and substantial diagnosis time. METHODS: One cytopathologist and two cytotechnologists reviewed 116 urine cytology slides and corresponding whole-slide images (WSIs) from urology patients. They used three diagnostic modalities: microscopy, WSI review, and AIxURO, per The Paris System for Reporting Urinary Cytology (TPS) criteria. Performance metrics, including TPS-guided and binary diagnosis, inter- and intraobserver agreement, and screening time, were compared across all methods and reviewers. RESULTS: AIxURO improved diagnostic accuracy by increasing sensitivity (from 25.0%-30.6% to 63.9%), positive predictive value (PPV; from 21.6%-24.3% to 31.1%), and negative predictive value (NPV; from 91.3%-91.6% to 95.3%) for atypical urothelial cell (AUC) cases. For suspicious for high-grade urothelial carcinoma (SHGUC) cases, it improved sensitivity (from 15.2%-27.3% to 33.3%), PPV (from 31.3%-47.4% to 61.1%), and NPV (from 91.6%-92.7% to 93.3%). Binary diagnoses exhibited an improvement in sensitivity (from 77.8%-82.2% to 90.0%) and NPV (from 91.7%-93.4% to 95.8%). Interobserver agreement across all methods showed moderate consistency (κ = 0.57-0.61), with the cytopathologist demonstrating higher intraobserver agreement than the two cytotechnologists across the methods (κ = 0.75-0.88). AIxURO significantly reduced screening time by 52.3%-83.2% from microscopy and 43.6%-86.7% from WSI review across all reviewers. Screening-positive (AUC+) cases required more time than negative cases across all methods and reviewers. CONCLUSIONS: AIxURO demonstrates the potential to improve both sensitivity and efficiency in bladder cancer diagnostics via urine cytology. Its integration into the cytopathological screening workflow could markedly decrease screening times, which would improve overall diagnostic processes.

Comparative evaluation of slide scanners, scan settings, and cytopreparations for digital urine cytology.

Background: Acquiring well-focused digital images of cytology slides with scanners can be challenging due to the 3-dimensional nature of the slides. This study evaluates performances of whole-slide images (WSIs) obtained from 2 different cytopreparations by 2 distinct scanners with 3 focus modes. Methods: Fourteen urine specimens were collected from patients with urothelial carcinoma. Each specimen was equally divided into 2 portions, prepared with Cytospin and ThinPrep methods and scanned for WSIs using Leica (Aperio AT2) and Hamamatsu (NanoZoomer S360) scanners, respectively. The scan settings included 3 focus modes (default, semi-auto, and manual) for single-layer scanning, along with a manual focus mode for 21 Z-layers scanning. Performance metrics were evaluated including scanning success rate, artificial intelligence (AI) algorithm-inferred atypical cell numbers and coverage rate (atypical cell numbers in single or multiple Z-layers divided by the total atypical cell numbers in 21 Z-layers), scanning time, and image file size. Results: The default mode had scanning success rates of 85.7% or 92.9%, depending on the scanner used. The semi-auto mode increased success to 92.9% or 100%, and manual even further to 100%. However, these changes did not affect the standardized median atypical cell numbers and coverage rates. The selection of scanners, cytopreparations, and Z-stacking influenced standardized median atypical cell numbers and coverage rates, scanning times, and image file sizes. Discussion: Both scanners showed satisfactory scanning. We recommend using semi-auto or manual focus modes to achieve a scanning success rate of up to 100%. Additionally, a minimum of 9-layer Z-stacking at 1 µm intervals is required to cover 80% of atypical cells. These advanced focus methods do not impact the number of atypical cells or their coverage rate. While Z-stacking enhances the AI algorithm's inferred quantity and coverage rates of atypical cells, it simultaneously results in longer scanning times and larger image file sizes.

Evaluation of an artificial intelligence algorithm for assisting the Paris System in reporting urinary cytology: A pilot study.

BACKGROUND: The Paris System for Reporting Urinary Cytology (TPS) has been shown to improve bladder cancer diagnosis. Advances in artificial intelligence (AI) may assist and improve the clinical workflow by applying TPS in routine diagnostic services. METHODS: A deep-learning-based algorithm was developed to identify urothelial cancer candidate cells using whole-slide images (WSIs). In the testing cohort, 131 urine cytology slides were retrospectively retrieved and analyzed using this AI algorithm. The authors compared the performance of one cytopathologist and two cytotechnologists using AI-assisted digital urine cytology. Then, the AI-assisted WSIs were evaluated in the clinical workflow. The cytopathologist first made a diagnosis by reviewing the AI-inferred WSIs and quantitative data (nuclear-to-cytoplasmic ratio and nuclear size) for each sample. After a washout period, the same cytopathologist made a diagnosis for the same samples using direct microscopy. All diagnosis results were compared with the expert panel consensus. RESULTS: The AI-assisted diagnosis by the two cytotechnologists and the one cytopathologist demonstrated performance results that were comparable to the expert panel consensus (sensitivity, 79.5% and 82.1% vs. 92.3%, respectively; specificity, 100% and 98.9% vs. 100%, respectively). Furthermore, the performance of the AI-assisted WSIs compared with the microscopic diagnosis by the cytopathologist demonstrated superior sensitivity (92.3% vs. 87.2%) and negative predictive value (96.8% vs. 94.8%). In addition, the AI-assisted reporting demonstrated near perfect agreement with the expert panel consensus (κ = 0.944) and the microscopic diagnosis (κ = 0.862). CONCLUSIONS: The AI algorithm developed by the authors effectively assisted TPS-based reporting by providing AI-inferred WSIs and quantitative data.

Novel Intraoral Negative Airway Pressure in Drug-Induced Sleep Endoscopy with Target-Controlled Infusion.

BACKGROUND: In intermittent negative airway pressure (iNAP) therapy, soft tissues are reshaped into a forward-resting position, thus reducing airway obstruction during sleep. This study investigated the effect of iNAP therapy that was administered during drug-induced sleep endoscopy with target-controlled infusion (TCI-DISE) in patients with obstructive sleep apnea (OSA) intolerant of continuous positive airway pressure (CPAP) therapy. METHODS: This prospective case series study included 92 patients with polysomnography (PSG)-confirmed OSA who underwent TCI-DISE with iNAP from January 2018 to February 2020 at a tertiary referral hospital. Upper airway obstruction was evaluated and scored using the velum, oropharynx, tongue base, and epiglottis (VOTE) classification. Obstruction severity was assessed multiple times with the patient in the supine position with or without lateral rotation of the head and the application of iNAP therapy, respectively. RESULTS: After the application of iNAP therapy in the supine position, obstruction severity decreased significantly: from complete or partial obstruction to partial or no obstruction in 37, 12, and 36 patients (40.2%, 13%, and 39%, respectively) with velar obstruction, oropharyngeal, and tongue base obstruction, respectively. After simultaneously applying iNAP therapy with head rotation, obstruction severity decreased in 47, 43, and 19 patients (51%, 47%, and 21%, respectively) with velar, tongue base, and epiglottic obstruction, respectively. CONCLUSION: In TCI-DISE, we found that iNAP therapy relieved velar, oropharyngeal, and tongue base obstruction in the supine position in some patients. Moreover, iNAP therapy can be combined with positional therapy to alleviate velar, tongue base, and epiglottic obstruction in some patients. TCI-DISE can also be used to screen the possible responders for iNAP therapy because it is less time consuming than PSG.

Building a model to precisely target the responders of a novel intermittent negative air pressure device-with mechanism definition.

OBJECTIVE: Due to the complexity of obstructive sleep apnea syndrome (OSAS), engaging patients in the right treatment poses a constant challenge. A novel oral pressure therapy device, the intermittent negative air pressure Sleep Therapy System (iNAP), has proven to ameliorate respiratory events for OSAS patients. However, the mode of action and the characteristics of its responders are not yet fully understood. Therefore, we have first disclosed the mechanism and provided systemic models to predict the treatment response. METHODS: Series of imaging studies were carried out to differentiate the anatomical features of iNAP responders versus non-responders. Compatible electroencephalography was used to evaluate sleep status during magnetic resonance imaging (MRI) assessments. RESULTS: The upper airway volume was statistically widened under the iNAP treatment while patients were naturally asleep (p < 0.05). Negative predictors included several parameters related to oral-tissue redundancy, enlarged middle pharyngeal space, and longer distance of hyoidale to mandibular plane. Positive predictors included larger angulation of sella-articulate-gonion, longer distance of anterior nasal spine to posterior nasal spine, and elongated tongue, which could correspond to the fact that the iNAP had a greater ability to widen the retropalatal region. Furthermore, algorithms developed by these predictors were built to predict treatment response. CONCLUSIONS: We were able to confirm the effect of the iNAP in widening the upper airway. Anatomic features that can be visually observed or obtained through X-ray films, accompanied with the resulting algorithms, were provided to facilitate physicians' ability to predict patients' treatment response to the iNAP with greater sensitivity and efficiency.