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For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient’s 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis. Accurate diagnosis of interstitial lung disease subtypes and prediction of patient survival rates remains challenging. Here, the authors develop AI algorithms to combine patient’s clinical history and longitudinal CT images to help clinicians diagnose and classify subtypes and dynamically predict disease progression and prognosis.

Deep learning-based machine learning models have shown significant results in speech recognition and numerous vision-related tasks. The performance of the present speech-to-text model relies upon the hyperparameters used in this research work. In this research work, it is shown that convolutional neural networks (CNNs) can model raw and tonal speech signals. Their performance is on par with existing recognition systems. This study extends the role of the CNN-based approach to robust and uncommon speech signals (tonal) using its own designed database for target research. The main objective of this research work was to develop a speech-to-text recognition system to recognize the tonal speech signals of Gurbani hymns using a CNN. Further, the CNN model, with six layers of 2DConv, 2DMax Pooling, and 256 dense layer units (Google’s TensorFlow service) was also used in this work, as well as Praat for speech segmentation. Feature extraction was enforced using the MFCC feature extraction technique, which extracts standard speech features and features of background music as well. Our study reveals that the CNN-based method for identifying tonal speech sentences and adding instrumental knowledge performs better than the existing and conventional approaches. The experimental results demonstrate the significant performance of the present CNN architecture by providing an 89.15% accuracy rate and a 10.56% WER for continuous and extensive vocabulary sentences of speech signals with different tones.

Background. We encounter interstitial lung disease (ILD) patients with psoriasis. The aim of this case series was to examine clinical and radiographic characteristics of patients with concomitant psoriasis and ILD. Methods. This is a retrospective review of our institutional experience of ILD concomitant with psoriasis, from the database in the Advanced Lung/Interstitial Lung Disease Program at the Mount Sinai Hospital. Out of 447 ILD patients, we identified 21 (4.7%) with antecedent or concomitant diagnosis of psoriasis. Clinical, radiographic, pathological, and outcome data were abstracted from our medical records. Results. Median age was 66 years (range, 46–86) and 14 (66.7%) were male. Thirteen (61.9%) had not previously or concomitantly been exposed to immunosuppressive therapy directed against psoriasis. Two (9.5%) ultimately died. Clinical diagnosis of ILD included idiopathic pulmonary fibrosis, 11 (52.4%); nonspecific interstitial pneumonia (NSIP), 2 (9.5%); cryptogenic organizing pneumonia, 2 (9.5%); chronic hypersensitivity pneumonitis, 2 (9.5%); and the others, while radiographic diagnosis included usual interstitial pneumonia pattern, 9 (42.9%); NSIP pattern, 6 (28.6%); organizing pneumonia pattern, 4 (19.0%); hypersensitivity pneumonitis pattern, 2 (9.5%); and the others. Conclusions. We report 21 ILD cases with antecedent or concomitant diagnosis of psoriasis. Further prospective studies are required to determine the association between ILD and psoriasis.

Case Vignette A 36-year old man with autosomal dominant polycystic kidney disease (ADPKD) was referred for evaluation of pulmonary cysts discovered incidentally on computed tomographic (CT) imaging of the abdomen and pelvis. On pulmonary function testing, the FVC was 2.77 L (57% predicted), the FEVt was 2.23 L (55% predicted), the total lung capacity was 4.68 L (69% predicted), and the diffusing capacity for carbon monoxide was 23.9 ml/mm Hg/min (68% predicted). Clinical Reasoning and Diagnosis Diffuse cystic lung diseases are a heterogeneous group of conditions in which multiple round, thin-walled, lucent cysts are found within the lung parenchyma. Other common manifestations include LAM and renal angiomyolipomas, skin manifestations (such as shagreen patches, hypomelanotic macules), brain cortical lesions, subependymal nodules, and giant cell astrocytomas (2). Because our patient's skin and brain lesions were not wholly explained by ADPKD alone, he was referred to a geneticist for further evaluation. A chromosome microarray (array Comparative Genomic Hybridization) from a peripheral blood specimen demonstrated a pathogenic deletion of 431.9 kb on chromosome 16p13.3 involving the tuberous sclerosis complex 2 (TSC2) and polycystic kidney disease 1 (PKD1) segments consistent with a diagnosis of TSC2-PKD1 contiguous gene deletion (CGD) syndrome. Loss-of-function mutations lead to cellular proliferation via up-regulated mammalian target of rapamycin (mTOR) signaling. According to the 2012 International Tuberous Sclerosis Complex Consensus Conference criteria, tuberous sclerosis can be diagnosed by genetic testing (which is sufficient for the diagnosis) or by major and/or minor clinical criteria (4). Other case reports describe large renal cysts discovered...