A Novel Automatic Algorithm to Support Lung Ultrasound Non-Expert Physicians in Interstitial Pneumonia Evaluation: A Single-Center Study.

INTRODUCTION: Lung ultrasound (LUS) is widely used in clinical practice for identifying interstitial lung diseases (ILDs) and assessing their progression. Although high-resolution computed tomography (HRCT) remains the gold standard for evaluating the severity of ILDs, LUS can be performed as a screening method or as a follow-up tool post-HRCT. Minimum training is needed to better identify typical lesions, and the integration of innovative artificial intelligence (AI) automatic algorithms may enhance diagnostic efficiency. AIM: This study aims to assess the effectiveness of a novel AI algorithm in automatic ILD recognition and scoring in comparison to an expert LUS sonographer. The "SensUS Lung" device, equipped with an automatic algorithm, was employed for the automatic recognition of the typical ILD patterns and to calculate an index grading of the interstitial involvement. METHODS: We selected 33 Caucasian patients in follow-up for ILDs exhibiting typical HRCT patterns (honeycombing, ground glass, fibrosis). An expert physician evaluated all patients with LUS on twelve segments (six per side). Next, blinded to the previous evaluation, an untrained operator, a non-expert in LUS, performed the exam with the SensUS device equipped with the automatic algorithm ("SensUS Lung") using the same protocol. Pulmonary functional tests (PFT) and DLCO were conducted for all patients, categorizing them as having reduced or preserved DLCO. The SensUS device indicated different grades of interstitial involvement named Lung Staging that were scored from 0 (absent) to 4 (peak), which was compared to the Lung Ultrasound Score (LUS score) by dividing it by the number of segments evaluated. Statistical analyses were done with Wilcoxon tests for paired values or Mann-Whitney for unpaired samples, and correlations were performed using Spearman analysis; p < 0.05 was considered significant. RESULTS: Lung Staging was non-inferior to LUS score in identifying the risk of ILDs (median SensUS 1 [0-2] vs. LUS 0.67 [0.25-1.54]; p = 0.84). Furthermore, the grade of interstitial pulmonary involvement detected with the SensUS device is directly related to the LUS score (r = 0.607, p = 0.002). Lung Staging values were inversely correlated with forced expiratory volume at first second (FEV1%, r = -0.40, p = 0.027), forced vital capacity (FVC%, r = -0.39, p = 0.03) and forced expiratory flow (FEF) at 25th percentile (FEF25%, r = -0.39, p = 0.02) while results directly correlated with FEF25-75% (r = 0.45, p = 0.04) and FEF75% (r = 0.43, p = 0.01). Finally, in patients with reduced DLCO, the Lung Staging was significantly higher, overlapping the LUS (reduced median 1 [1-2] vs. preserved 0 [0-1], p = 0.001), and overlapping the LUS (reduced median 18 [4-20] vs. preserved 5.5 [2-9], p = 0.035). CONCLUSIONS: Our data suggest that the considered AI automatic algorithm may assist non-expert physicians in LUS, resulting in non-inferior-to-expert LUS despite a tendency to overestimate ILD lesions. Therefore, the AI algorithm has the potential to support physicians, particularly non-expert LUS sonographers, in daily clinical practice to monitor patients with ILDs. The adopted device is user-friendly, offering a fully automatic real-time analysis. However, it needs proper training in basic skills.

Exhaled inflammatory markers in aspirin-induced asthma syndrome.

BACKGROUND: Interleukin (IL)-4 and IL-6, respectively, markers of neutrophilic and eosinophilic inflammation, were analyzed in nasal and oral exhaled breath condensate to understand the inflammation of upper and lower airways in subjects with aspirin-induced asthma (AIA) syndrome, evaluating possible differences between AIA and the single pathological conditions included in AIA syndrome. METHODS: Twelve patients with AIA, 17 patients with mild asthma (MA), 12 patients with nasal polyposis (NP), 11 patients with mild asthma + nasal polyposis (MA + NP), and 10 healthy subjects (HSs) were enrolled. Nasal and oral exhaled IL-4 and IL-6 were measured by enzyme immunoassay kit. RESULTS: Higher levels of nasal and oral exhaled IL-4 and IL-6 were observed in AIA compared with MA, NP, MA + NP, and HSs. Moreover, a positive correlation was identified between nasal exhaled IL-4 and IL-6 and, respectively, the number of neutrophils and eosinophils and in nasal scraping. CONCLUSION: The concentration of eosinophilic and neutrophilic markers in upper and lower airways of subjects with AIA syndrome is higher compared with HS and subjects with MA, NP, and MA + NP.

Sleep related breathing disorders in adults with Down syndrome.

BACKGROUND: While the prevalence of obstructive sleep apnoea syndrome among children with Down syndrome is reported to vary from 30 to 50%, the nocturnal respiratory patterns of adults with Down syndrome is not well known. OBJECTIVES: The aim of this study is to evaluate sleep-related breathing disorders in a sample of adults with Down syndrome. METHODS: We studied the nocturnal respiratory patterns of 6 adults with Down syndrome, aged 28-53 years. All participants were monitored for 8 hours using a 12 channel polysomnograph. Respiratory events (apnoeic and hypopnoeic) were classified as obstructive or central, in relation to the presence or the absence of paradoxical breathing. RESULTS: All participants had respiratory pauses during sleep. 5 of them had an apnoea/hypopnoea index > 10, justifying the diagnosis of sleep apnoea syndrome. About 85% of the respiratory events were apnoeic, the others being hypopnoeic. Among all the respiratory events 89.2% were obstructive, whereas only 10.8% were central. The central events were almost always organised in very low and regular sequences and respiration frequently showed a true periodic pattern, consisting of short periods of augmented breath followed by central events causing oxygen desaturation. CONCLUSIONS: According to the literature and in conjunction with the current study's results it could be hypothesised that the nocturnal respiratory pattern of adults with Down syndrome depends on several pathogenetic factors such as age, severity of upper airway abnormalities, body mass index (BMI), other pathological conditions and age-related brainstem dysfunction.