A case of complete recovery of negative pressure pulmonary edema after a Cottle surgery in a 24-year-old male. Teaching point: Negative pressure pulmonary edema is an important cause of postoperative noncardiogenic edema, with the spontaneous disappearance of all complaints within a relatively short period.

BACKGROUND: Mounier-Kuhn syndrome (MKS) is a rare disorder characterized by abnormal dilation of the trachea and main bronchi. MKS can be easily missed on chest X-rays, making diagnosis difficult. Under general anesthesia, challenges such as airway leakage or collapse during mechanical ventilation may complicate the achievement of adequate tidal volumes.
METHODS: A 94-year-old woman requiring emergency hemiarthroplasty of the hip under general anesthesia was admitted. Preoperative chest X-rays revealed dilation of the trachea and main bronchi, but the patient exhibited no respiratory symptoms. We diagnosed her with MKS and opted for an 8.0-mm-inner-diameter reinforced tracheal tube. We positioned the cuff in the subglottic area, inflating it while monitoring for air leakage. Throughout the surgery, adequate tidal volume was maintained.
CONCLUSIONS: Anesthesiologists must conduct a comprehensive evaluation of patients with MKS, including a review of chest radiographs, and establish a meticulous anesthesia plan prior to surgery.

OBJECTIVE: Artificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools.
METHODS: Findings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports.
RESULTS: Chest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001).
CONCLUSIONS: The AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future.
UNASSIGNED: With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest.
CONCLUSIONS: • A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow.

Central airway obstruction often presents with respiratory symptoms identical to those of common respiratory diseases. Diagnosis of central airway obstruction is challenging in clinical practice, especially misdiagnosed as asthma or chronic obstructive pulmonary disease in case of \"normal\" chest X-ray. Here, we reported five central airway obstruction cases: the first and fourth cases misdiagnosed as asthma, the second masquerading as chronic obstructive pulmonary disease exacerbation, the third diagnosed incorrectly with non-resolving pneumonia, and the fifth misdiagnosed as chronic obstructive pulmonary disease. We then analyzed diagnostic clues potentially useful to differentiate central airway obstruction from asthma/chronic obstructive pulmonary disease. A multidisciplinary approach to manage central airway obstruction is essential, particularly selecting judiciously the method of respiratory support due to the high risk of completed airway collapse or obstruction during interventional period.

Atelectasis is defined as the the loss of lung volume due to collapse of lung tissue and is usually associated with symptoms of respiratory distress, including increased work of breathing and increased oxygen requirements. It is common in hospitalized patients with limited mobility and in patients with underlying lung conditions. Treatment is largely supportive when no underlying condition is identified. It is rare to occur in otherwise healthy individuals. The patient in this case presented to the emergency department with complaints of progressive shortness of breath, productive cough, chest tightness, subjective fever, chills, and nasal congestion for two weeks. Physical exam revealed decreased breath sounds on the left side, raising the suspicion for atelectasis or pneumothorax. Chest X-ray revealed complete white-out of the left lung. Bronchoscopy was performed and revealed mucus impaction in the left mainstem bronchus, which was removed. Repeat chest X-ray revealed resolution of atelectasis. The patient\'s symptoms improved, and she was discharged with outpatient pulmonary follow-up. The case described below illustrates that even in young patients with no underlying comorbidities, other than obesity, atelectasis as a cause of respiratory complaints should always be considered.

BACKGROUND: Chest X-ray offers high sensitivity and acceptable specificity as a tuberculosis screening tool, but in areas with a high burden of tuberculosis, there is often a lack of radiological expertise to interpret chest X-ray. Computer-aided detection systems based on artificial intelligence are therefore increasingly used to screen for tuberculosis-related abnormalities on digital chest radiographies. The CAD4TB software has previously been shown to demonstrate high sensitivity for chest X-ray tuberculosis-related abnormalities, but it is not yet calibrated for the detection of non-tuberculosis abnormalities. When screening for tuberculosis, users of computer-aided detection need to be aware that other chest pathologies are likely to be as prevalent as, or more prevalent than, active tuberculosis. However, non--tuberculosis chest X-ray abnormalities detected during chest X-ray screening for tuberculosis remain poorly characterized in the sub-Saharan African setting, with only minimal literature.
METHODS: In this case series, we report on four cases with non-tuberculosis abnormalities detected on CXR in TB TRIAGE + ACCURACY (ClinicalTrials.gov Identifier: NCT04666311), a study in adult presumptive tuberculosis cases at health facilities in Lesotho and South Africa to determine the diagnostic accuracy of two potential tuberculosis triage tests: computer-aided detection (CAD4TB v7, Delft, the Netherlands) and C-reactive protein (Alere Afinion, USA). The four Black African participants presented with the following chest X-ray abnormalities: a 59-year-old woman with pulmonary arteriovenous malformation, a 28-year-old man with pneumothorax, a 20-year-old man with massive bronchiectasis, and a 47-year-old woman with aspergilloma.
CONCLUSIONS: Solely using chest X-ray computer-aided detection systems based on artificial intelligence as a tuberculosis screening strategy in sub-Saharan Africa comes with benefits, but also risks. Due to the limitation of CAD4TB for non-tuberculosis-abnormality identification, the computer-aided detection software may miss significant chest X-ray abnormalities that require treatment, as exemplified in our four cases. Increased data collection, characterization of non-tuberculosis anomalies and research on the implications of these diseases for individuals and health systems in sub-Saharan Africa is needed to help improve existing artificial intelligence software programs and their use in countries with high tuberculosis burden.

BACKGROUND: According to the World Bank, Malaysia reported an estimated 97 tuberculosis cases per 100,000 people in 2021. Chest x-ray (CXR) remains the best conventional method for the early detection of pulmonary tuberculosis (PTB) infection. The intervention of artificial intelligence (AI) in PTB diagnosis could efficiently aid human interpreters and reduce health professionals\' work burden. To date, no AI studies have been evaluated in Malaysia.
OBJECTIVE: This study aims to evaluate the performance of Putralytica and Qure.ai software for CXR screening and PTB diagnosis among the Malaysian population.
METHODS: We will conduct a retrospective case-control study at the Respiratory Medicine Institute, National Cancer Institute, and Sungai Buloh Health Clinic. A total of 1500 CXR images of patients who completed treatments or check-ups will be selected and categorized into three groups: (1) abnormal PTB cases, (2) abnormal non-PTB cases, and (3) normal cases. These CXR images, along with their clinical findings, will be the reference standard in this study. All patient data, including sociodemographic characteristics and clinical history, will be collected prior to screening via Putralytica and Qure.ai software and readers\' interpretation, which are the index tests for this study. Interpretation from all 3 index tests will be compared with the reference standard, and significant statistical analysis will be computed.
RESULTS: Data collection is expected to commence in August 2023. It is anticipated that 1 year will be needed to conduct the study.
CONCLUSIONS: This study will measure the accuracy of Putralytica and Qure.ai software and whether their findings will concur with readers\' interpretation and the reference standard, thus providing evidence toward the effectiveness of implementing AI in the medical setting.
UNASSIGNED: PRR1-10.2196/36121.

Hypersensitivity pneumonitis (HP) is a lung disease in which foreign matter is inhaled and exposed to lung parenchymal and interstitial tissue. Such matter may include pollen, molds, chemicals, and smoke. HP leads to widespread inflammation and even fibrosis in chronic forms; the main route of treatment usually involves corticosteroids and antifibrotics as needed. We describe a patient case in which HP was diagnosed after using recreational marijuana, and her chest x-ray had a complete resolution after one day of a corticosteroid regimen. As recreational marijuana use increases, clinicians need to keep HP on the differential diagnosis in patients that frequently utilize recreational marijuana obtained through illicit business.

A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered \"gold standard\". Matthew\'s correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to \"gold standard\" (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology.

Patients presenting to the emergency room with respiratory symptoms often receive a chest X-ray as part of the initial workup to exclude common pathologies. A 60-year-old patient presented to the emergency department with dyspnoea. An initial chest X-ray revealed the Fleischner\'s sign, the knuckle sign, and the Westermark sign, specific but not sensitive for central pulmonary embolism, prompting a follow-up angio CT to confirm the diagnosis. The patient was treated and discharged a few days later.
UNASSIGNED: Chest X-rays, done as part of an initial workup, can show signs of pathologies that are not yet clinically suspected, such as pulmonary embolism.