Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study.

BACKGROUND: Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach. METHODS: A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls. DISCUSSION: This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022. CLINICALTRIALS: gov Identifier: NCT05318599.

Specificity in rehabilitation of word production: a meta-analysis and a case study.

Speech production impairment is a frequent deficit observed in aphasic patients and rehabilitation programs have been extensively developed. Nevertheless, there is still no agreement on the type of rehabilitation that yields the most successful outcomes. Here, we ran a detailed meta-analysis of 39 studies of word production rehabilitation involving 124 patients. We used a model-driven approach for analyzing each rehabilitation task by identifying which levels of our model each task tapped into. We found that (1) all rehabilitation tasks are not equally efficient and the most efficient ones involved the activation of the two levels of the word production system: the phonological output lexicon and the phonological output, and (2) the activation of the speech perception system as it occurs in many tasks used in rehabilitation is not successful in rehabilitating word production. In this meta-analysis, the effect of the activation of the phonological output lexicon and the phonological output cannot be assessed separately. We further conducted a rehabilitation study with DPI, a patient who suffers from a damage of the phonological output lexicon. Our results confirm that rehabilitation is more efficient, in terms of time and performance, when specifically addressing the impaired level of word production.

The impairment of emotion recognition in Huntington's disease extends to positive emotions.

Patients with Huntington's Disease (HD) are impaired in the recognition of emotional signals. However, the nature and extent of the impairment is controversial: it has variously been argued to disproportionately affect disgust (e.g., Sprengelmeyer et al., 1996), to be general for negative emotions (Snowden et al., 2008), or to be a consequence of item difficulty (Milders et al., 2003). Yet no study to date has included more than one positive stimulus category in emotion recognition tasks, and most studies have focused on the recognition of emotions from facial stimuli. In this study, we test the hypothesis that patients with HD may be impaired in their recognition of positive as well as negative emotional signals, by examining the recognition of a range of positive emotions from vocal cues. We present a study of 14 Huntington's patients and 15 controls performing a forced-choice task with a previously validated set of negative and positive non-verbal emotional vocalizations (Sauter and Scott, 2007). Although HD patients performed above chance for each emotion, they were found to be impaired in both positive and negative emotions, including pleasure, fear and anger. These findings complement previous work by demonstrating that impairments in emotion recognition in HD extend to positive and negative emotions, which may imply a general deficit.