Pulmonary disease identification and characterization are among the most intriguing research topics of recent years since they require an accurate and prompt diagnosis. Although pulmonary radiography has helped in lung disease diagnosis, the interpretation of the radiographic image has always been a major concern for doctors and radiologists to reduce diagnosis errors. Due to their success in image classification and segmentation tasks, cutting-edge artificial intelligence techniques like machine learning (ML) and deep learning (DL) are widely encouraged to be applied in the field of diagnosing lung disorders and identifying them using medical images, particularly radiographic ones. For this end, the researchers are concurring to build systems based on these techniques in particular deep learning ones. In this paper, we proposed three deep-learning models that were trained to identify the presence of certain lung diseases using thoracic radiography. The first model, named \"CovCXR-Net\", identifies the COVID-19 disease (two cases: COVID-19 or normal). The second model, named \"MDCXR3-Net\", identifies the COVID-19 and pneumonia diseases (three cases: COVID-19, pneumonia, or normal), and the last model, named \"MDCXR4-Net\", is destined to identify the COVID-19, pneumonia and the pulmonary opacity diseases (4 cases: COVID-19, pneumonia, pulmonary opacity or normal). These models have proven their superiority in comparison with the state-of-the-art models and reached an accuracy of 99,09 %, 97.74 %, and 90,37 % respectively with three benchmarks.

OBJECTIVE: To evaluate an artificial intelligence (AI)-assisted double reading system for detecting clinically relevant missed findings on routinely reported chest radiographs.
METHODS: A retrospective study was performed in two institutions, a secondary care hospital and tertiary referral oncology centre. Commercially available AI software performed a comparative analysis of chest radiographs and radiologists\' authorised reports using a deep learning and natural language processing algorithm, respectively. The AI-detected discrepant findings between images and reports were assessed for clinical relevance by an external radiologist, as part of the commercial service provided by the AI vendor. The selected missed findings were subsequently returned to the institution\'s radiologist for final review.
RESULTS: In total, 25,104 chest radiographs of 21,039 patients (mean age 61.1 years ± 16.2 [SD]; 10,436 men) were included. The AI software detected discrepancies between imaging and reports in 21.1% (5289 of 25,104). After review by the external radiologist, 0.9% (47 of 5289) of cases were deemed to contain clinically relevant missed findings. The institution\'s radiologists confirmed 35 of 47 missed findings (74.5%) as clinically relevant (0.1% of all cases). Missed findings consisted of lung nodules (71.4%, 25 of 35), pneumothoraces (17.1%, 6 of 35) and consolidations (11.4%, 4 of 35).
CONCLUSIONS: The AI-assisted double reading system was able to identify missed findings on chest radiographs after report authorisation. The approach required an external radiologist to review the AI-detected discrepancies. The number of clinically relevant missed findings by radiologists was very low.
CONCLUSIONS: The AI-assisted double reader workflow was shown to detect diagnostic errors and could be applied as a quality assurance tool. Although clinically relevant missed findings were rare, there is potential impact given the common use of chest radiography.
CONCLUSIONS: • A commercially available double reading system supported by artificial intelligence was evaluated to detect reporting errors in chest radiographs (n=25,104) from two institutions. • Clinically relevant missed findings were found in 0.1% of chest radiographs and consisted of unreported lung nodules, pneumothoraces and consolidations. • Applying AI software as a secondary reader after report authorisation can assist in reducing diagnostic errors without interrupting the radiologist\'s reading workflow. However, the number of AI-detected discrepancies was considerable and required review by a radiologist to assess their relevance.

Thoracic radiography and abdominal ultrasonography are part of standard diagnostic investigations in cases of canine immune-mediated polyarthritis (IMPA). However, the clinical importance of thoracic and abdominal imaging towards the management of canine IMPA currently remains unknown. The primary aim of this study was to describe the findings documented on thoracic radiography and abdominal ultrasonography in dogs diagnosed with IMPA, and to evaluate the diagnostic utility of thoracic radiography and abdominal ultrasonography in the initial approach and management of these cases. Seventy-seven dogs diagnosed with IMPA who underwent thoracic radiography and abdominal ultrasonography at a single referral hospital between 2008 and 2022 were included. The diagnostic imaging studies of these 77 dogs were reviewed by one blinded board-certified diagnostic imaging specialist for quality assurance. The medical records, including the diagnostic imaging reports of these dogs, were then reviewed by three blinded board-certified internal medicine specialists. Using a modified version of a previous question and scoring system, the three internal medicine specialists then generated an answer for the overall diagnostic utility and a diagnostic utility score for thoracic radiography and abdominal ultrasonography for each case. The abnormal findings identified in radiography and ultrasonography were described. In the cases where the findings were considered significant enough to immediately affect the case management, the results of the further investigations that were subsequently performed were also described. No abnormalities were detected in thoracic radiography for 30 cases, and none were detected in abdominal ultrasound for 6. The majority of the internists considered thoracic radiography to be not useful in the overall case management at the time of IMPA diagnosis in 70 cases, and considered abdominal ultrasonography to be not useful in the overall case management in 57 cases. The majority of the internists agreed on the utility of thoracic radiography in 95% of the cases, and in 61% of the cases for abdominal ultrasonography. The most common finding in the thoracic radiography was a mild bronchial pulmonary pattern, and the most common in the abdominal ultrasonography was mild lymphadenomegaly. Therefore, although thoracic radiography and abdominal ultrasonography identified numerous abnormal findings in this population of dogs, in the majority of the cases, the findings were deemed not useful towards the overall case management at the time of the initial diagnosis of IMPA. Thus, the use of thoracic radiography and abdominal ultrasonography should be taken into careful consideration when considering initial diagnostic investigations for canine IMPA.

OBJECTIVE: To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs.
METHODS: To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM.
RESULTS: DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC: 0.83 [95% CI: 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC: 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01).
CONCLUSIONS: A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC.
CONCLUSIONS: Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity.
CONCLUSIONS: • A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. • The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. • A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.

UNASSIGNED: In Thailand, epidemiological data on silicosis in the ceramic sector is lacking and the underdiagnosis of silicosis remains an extensive concern. Therefore, this study aimed to determine the prevalence of silicosis and the extent of underdiagnosis among Thai ceramic workers by reinterpreting chest radiographs previously taken by a health check-up unit.
UNASSIGNED: This retrospective cross-sectional study was conducted on ceramic workers undergoing health surveillance using chest radiographs in one ceramic factory in September 2018. All chest radiographs were done retrospectively, then were reinterpreted by professional readers specially trained in using the ILO International Classification of Radiograph of Pneumoconioses (ILO/ICRP). Chest radiographs with a profusion of 1/1 or greater were suggestive of silicosis.
UNASSIGNED: Out of the 244 participants undergoing chest radiography, the prevalence of silicosis was 2.9%. Overall, the mean age of the participants was 41 years, and 72.1% were female. Among individuals with silicosis, the median age was 43 years; 71.4% were male; the average employment duration was 26.9 years; while the male sex was the significant variable associated with silicosis with an odds ratio of 7.01 (95% confidence interval 1.31 to 37.4). Regarding the underdiagnosis, the health check-up unit failed to recognize all individuals with silicosis, and could not detect any radiographic chest abnormalities in 57.1% of those with silicosis.
UNASSIGNED: Despite the low prevalence of silicosis among Thai ceramic workers, this finding indicates ongoing exposure to silica in the ceramic industry. In addition, a significant proportion of the silicosis cases were underrecognized. Future efforts to prevent underdiagnosis and improve an occupational health surveillance service in Thailand are needed.

BACKGROUND: Severe courses and high hospitalization rates were ubiquitous during the first pandemic SARS-CoV-2 waves. Thus, we aimed to examine whether integrative diagnostics may aid in identifying vulnerable patients using crucial data and materials obtained from COVID-19 patients hospitalized between 2020 and 2021 (n = 52). Accordingly, we investigated the potential of laboratory biomarkers, specifically the dynamic cell decay marker cell-free DNA and radiomics features extracted from chest CT.
METHODS: Separate forward and backward feature selection was conducted for linear regression with the Intensive-Care-Unit (ICU) period as the initial target. Three-fold cross-validation was performed, and collinear parameters were reduced. The model was adapted to a logistic regression approach and verified in a validation naïve subset to avoid overfitting.
RESULTS: The adapted integrated model classifying patients into \"ICU/no ICU demand\" comprises six radiomics and seven laboratory biomarkers. The models\' accuracy was 0.54 for radiomics, 0.47 for cfDNA, 0.74 for routine laboratory, and 0.87 for the combined model with an AUC of 0.91.
CONCLUSIONS: The combined model performed superior to the individual models. Thus, integrating radiomics and laboratory data shows synergistic potential to aid clinic decision-making in COVID-19 patients. Under the need for evaluation in larger cohorts, including patients with other SARS-CoV-2 variants, the identified parameters might contribute to the triage of COVID-19 patients.

OBJECTIVE: To determine associations between antibody serologic tests and tracheobronchial lymphadenopathy (TBL) in dogs with pulmonary coccidioidomycosis and identify variables associated with time to resolution of TBL.
METHODS: 32 client owned dogs with newly diagnosed pulmonary coccidioidomycosis from October 2020 to February 2021.
METHODS: Prospective cohort study. Thoracic radiographs and anti-Coccidioides spp antibody serology were performed at baseline and once every 3 months until remission or for a maximum of 12 months. Radiographic tracheobronchial lymph node height, length, and area were measured and recorded as ratios via comparison with the length of the T4 vertebral body (LT4) and length of the manubrium. Severity of TBL was also subjectively categorized as mild, moderate, or severe.
RESULTS: Tracheobronchial lymphadenopathy was identified in 81% (26/32; 95% CI, 64% to 93%) of dogs. There was no relevant association between TBL presence or severity and antibody serology results. Tracheobronchial lymphadenopathy resolved in 72% (n = 18) of dogs at the 3-month evaluation. The median time to resolution of TBL after initiation of fluconazole was 96 days (range, 72 to 386 days). Univariate analysis identified increasing TBL severity (hazard ratio, 0.40; 95% CI, 0.19 to 0.84; P = .02) and length:LT4 ratio (hazard ratio, 0.41; 95% CI, 0.20 to 0.82; P = .01) as variables associated with reduced probability of resolution of TBL.
CONCLUSIONS: Antibody serologic test results are not clinically useful to predict TBL presence or severity in dogs with pulmonary coccidioidomycosis, and larger tracheobronchial lymph nodes are more likely to take longer to resolve. Resolution of TBL occurs in most dogs within 3 to 6 months after fluconazole administration.

Description Lung cavitation as a complication of COVID-19 is rare. A 56-year-old male presented with lung cavitation, small volume hemoptysis, and violaceous discoloration of the right great toe, 5 weeks after diagnosis with COVID-19 pneumonia. The digital changes were consistent with previously described microvascular changes called \"COVID toe.\" CT angiography of the chest was negative for pulmonary embolism but showed a 2.5 x 3.1 x 2.2 cm cavitation within the right lung. Extensive evaluation for commonly implicated infectious and autoimmune causes was negative. We concluded that the cavitary lung lesions were likely a complication of COVID-19 pneumonia and may implicate microangiopathy as an important component of pathogenesis. This case highlights a rare complication of COVID-19 of which clinicians should be aware.

Suboptimal chest radiographs (CXR) can limit interpretation of critical findings. Radiologist-trained AI models were evaluated for differentiating suboptimal(sCXR) and optimal(oCXR) chest radiographs.
Our IRB-approved study included 3278 CXRs from adult patients (mean age 55 ± 20 years) identified from a retrospective search of CXR in radiology reports from 5 sites. A chest radiologist reviewed all CXRs for the cause of suboptimality. The de-identified CXRs were uploaded into an AI server application for training and testing 5 AI models. The training set consisted of 2202 CXRs (n = 807 oCXR; n = 1395 sCXR) while 1076 CXRs (n = 729 sCXR; n = 347 oCXR) were used for testing. Data were analyzed with the Area under the curve (AUC) for the model\'s ability to classify oCXR and sCXR correctly.
For the two-class classification into sCXR or oCXR from all sites, for CXR with missing anatomy, AI had sensitivity, specificity, accuracy, and AUC of 78%, 95%, 91%, 0.87(95% CI 0.82-0.92), respectively. AI identified obscured thoracic anatomy with 91% sensitivity, 97% specificity, 95% accuracy, and 0.94 AUC (95% CI 0.90-0.97). Inadequate exposure with 90% sensitivity, 93% specificity, 92% accuracy, and AUC of 0.91 (95% CI 0.88-0.95). The presence of low lung volume was identified with 96% sensitivity, 92% specificity, 93% accuracy, and 0.94 AUC (95% CI 0.92-0.96). The sensitivity, specificity, accuracy, and AUC of AI in identifying patient rotation were 92%, 96%, 95%, and 0.94 (95% CI 0.91-0.98), respectively.
The radiologist-trained AI models can accurately classify optimal and suboptimal CXRs. Such AI models at the front end of radiographic equipment can enable radiographers to repeat sCXRs when necessary.

The role of preoperative chest radiography (CR) for prediction of postoperative pneumonia remains uncertain. We aimed to develop and validate a prediction model for postoperative pneumonia incorporating findings of preoperative CRs evaluated by a deep learning-based computer-aided detection (DL-CAD) system MATERIALS AND METHODS: This retrospective study included consecutive patients who underwent surgery between January 2019 and March 2020 and divided into development (surgery in 2019) and validation (surgery between January and March 2020) cohorts. Preoperative CRs obtained within 1-month before surgery were analyzed with a commercialized DL-CAD that provided probability values for the presence of 10 different abnormalities in CRs. Logistic regression models to predict postoperative pneumonia were built using clinical variables (clinical model), and both clinical variables and DL-CAD results for preoperative CRs (DL-CAD model). The discriminative performances of the models were evaluated by area under the receiver operating characteristic curves.
In development cohort (n = 19,349; mean age, 57 years; 11,392 men), DL-CAD results for pulmonary nodules (odds ratio [OR, for 1% increase in probability value], 1.007; p = 0.021), consolidation (OR, 1.019; p < 0.001), and cardiomegaly (OR, 1.013; p < 0.001) were independent predictors of postoperative pneumonia and were included in the DL-CAD model. In validation cohort (n = 4957; mean age, 56 years; 2848 men), the DL-CAD model exhibited a higher AUROC than the clinical model (0.843 vs. 0.815; p = 0.012).
Abnormalities in preoperative CRs evaluated by a DL-CAD were independent risk factors for postoperative pneumonia. Using DL-CAD results for preoperative CRs led to an improved prediction of postoperative pneumonia.