BACKGROUND: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality.
OBJECTIVE: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data.
METHODS: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips.
RESULTS: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts\' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance.
CONCLUSIONS: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.

OBJECTIVE: Hyperpolarized xenon (129Xe) MRI is a noninvasive method to assess pulmonary structure and function. To measure lung microstructure, diffusion-weighted imaging-commonly the apparent diffusion coefficient (ADC)-can be employed to map changes in alveolar-airspace size resulting from normal aging and pulmonary disease. However, low signal-to-noise ratio (SNR) decreases ADC measurement certainty, and biases ADC to spuriously low values. Further, these challenges are most severe in regions of the lung where alveolar simplification or emphysematous remodeling generate abnormally high ADCs. Here, we apply Global Local Higher Order Singular Value Decomposition (GLHOSVD) denoising to enhance image SNR, thereby reducing uncertainty and bias in diffusion measurements.
METHODS: GLHOSVD denoising was employed in simulated images and gas phantoms with known diffusion coefficients to validate its effectiveness and optimize parameters for analysis of diffusion-weighted 129Xe MRI. GLHOSVD was applied to data from 120 subjects (34 control, 39 cystic fibrosis (CF), 27 lymphangioleiomyomatosis (LAM), and 20 asthma). Image SNR, ADC, and distributed diffusivity coefficient (DDC) were compared before and after denoising using Wilcoxon signed-rank analysis for all images.
RESULTS: Denoising significantly increased SNR in simulated, phantom, and in-vivo images, showing a greater than 2-fold increase (p < 0.001) across diffusion-weighted images. Although mean ADC and DDC remained unchanged (p > 0.05), ADC and DDC standard deviation decreased significantly in denoised images (p < 0.001).
CONCLUSIONS: When applied to diffusion-weighted 129Xe images, GLHOSVD improved image quality and allowed airspace size to be quantified in high-diffusion regions of the lungs that were previously inaccessible to measurement due to prohibitively low SNR, thus providing insights into disease pathology.

UNASSIGNED: Systemic Lupus Erythematosus (SLE) can have an insidious onset and a fatal prognosis in children. Patients presenting without typical signs of SLE should undergo further evaluation if they are not responding to the initial diagnosis and treatment. This is especially true for patients with rapidly progressing symptoms and deterioration in spite of treatment.
UNASSIGNED: Pediatric Systemic Lupus Erythematosus is a chronic autoimmune disorder with various organ involvement pulmonary involvement in the course of this disorder is a rare yet potentially life-threatening complication. In this case report we highlight the findings of a 16-year-old girl acutely and initially presenting with cough and fever, eventually complicating to diffuse alveolar hemorrhage and gradual loss of consciousness. Although the patient was started on immunosuppressive treatment after the diagnosis of lupus, based on renal and hematological impairment, was made and initially responded, she eventually deteriorated.

UNASSIGNED: Tuberculosis (TB) remains endemic in Singapore. Singapore\'s clinical practice guidelines for the management of tuberculosis were first published in 2016. Since then, there have been major new advances in the clinical management of TB, ranging from diagnostics to new drugs and treatment regimens. The National TB Programme convened a multidisciplinary panel to update guidelines for the clinical management of drug-susceptible TB infection and disease in Singapore, contextualising current evidence for local practice.
UNASSIGNED: Following the ADAPTE framework, the panel systematically reviewed, scored and synthesised English-language national and international TB clinical guidelines published from 2016, adapting recommendations for a prioritised list of clinical decisions. For questions related to more recent advances, an additional primary literature review was conducted via a targeted search approach. A 2-round modified Delphi process was implemented to achieve consensus for each recommendation, with a final round of edits after consultation with external stakeholders.
UNASSIGNED: Recommendations for 25 clinical questions spanning screening, diagnosis, selection of drug regimen, monitoring and follow-up of TB infection and disease were formulated. The availability of results from recent clinical trials led to the inclusion of shorter treatment regimens for TB infection and disease, as well as consensus positions on the role of newer technologies, such as computer-aided detection-artificial intelligence products for radiological screening of TB disease, next-generation sequencing for drug-susceptibility testing, and video observation of treatment.
UNASSIGNED: The panel updated recommendations on the management of drug-susceptible TB infection and disease in Singapore.

BACKGROUND: Digitally assisted health care services and technologies are gaining popularity. They assist patients in managing their conditions, thereby reducing the burden on health care staff. Digital health care enables individuals to receive care that is more tailored to their needs and preferences. When implemented properly, it can promote equity by considering each person\'s opportunities and limitations in the context of health care needs, preferences, values, and capabilities.
OBJECTIVE: This study aims to understand the needs, values, and preferences of individuals with chronic obstructive pulmonary disease (COPD) who are provided with a 24/7 digital health care service. Furthermore, we aim to understand the dynamics of the communities to which they belong and how these communities intersect. This will provide us with the essential knowledge to establish new methods of providing education, including the development of educational activities for health professionals to engage, train, and empower people living with COPD.
METHODS: The study included 7 informants diagnosed with COPD who received 24/7 digital health care service support from a regional project in Region Zealand, Denmark. The informants were visited 4 times during 2 months, including a \"Hello\" visit, a day with a semistructured interview, and 2 days with field observations. The informants participated in a semistructured interview, following participant observation and an ethnographic approach. The interview content was analyzed using an inductive methodology to categorize the empirical data.
RESULTS: Using the inductive approach, we identified 3 main categories related to the informants\' needs, values, and preferences: (1) Health, (2) Value Creation, and (3) Resources. These 3 main categories were based on 9 subcategories: (1) health and barriers, (2) self-monitoring, (3) medication, (4) behavior, (5) motivation, (6) hobbies, (7) social networks, (8) health professionals, and (9) technology. These findings revealed that the informants placed value on maintaining their daily activities and preserving their sense of identity before the onset of COPD. Furthermore, they expressed a desire not to be defined by their COPD, as conversations about COPD often shifted away from the topic.
CONCLUSIONS: Digital health solutions and the health care professionals who offer them should prioritize the individuals they serve, considering their needs, values, and preferences rather than solely focusing on the medical condition. This approach ensures the highest level of daily living and empowerment for those living with long-term health conditions. The communities surrounding individuals must engage in constant interaction and collaboration. They should work together to incorporate people\'s needs, values, and preferences into future digital health services, thereby promoting empowerment and self-management. New educational programs aimed at developing the digital health service competencies of registered nurses should facilitate collaboration between the 2 communities. This collaboration is essential for supporting patients with long-term health conditions in their daily activities.

UNASSIGNED: The use of chronic disease information systems in hospitals and communities plays a significant role in disease prevention, control, and monitoring. However, there are several limitations to these systems, including that the platforms are generally isolated, the patient health information and medical resources are not effectively integrated, and the \"Internet Plus Healthcare\" technology model is not implemented throughout the patient consultation process.
UNASSIGNED: The aim of this study was to evaluate the efficiency of the application of a hospital case management information system in a general hospital in the context of chronic respiratory diseases as a model case.
UNASSIGNED: A chronic disease management information system was developed for use in general hospitals based on internet technology, a chronic disease case management model, and an overall quality management model. Using this system, the case managers provided sophisticated inpatient, outpatient, and home medical services for patients with chronic respiratory diseases. Chronic respiratory disease case management quality indicators (number of managed cases, number of patients accepting routine follow-up services, follow-up visit rate, pulmonary function test rate, admission rate for acute exacerbations, chronic respiratory diseases knowledge awareness rate, and patient satisfaction) were evaluated before (2019-2020) and after (2021-2022) implementation of the chronic disease management information system.
UNASSIGNED: Before implementation of the chronic disease management information system, 1808 cases were managed in the general hospital, and an average of 603 (SD 137) people were provided with routine follow-up services. After use of the information system, 5868 cases were managed and 2056 (SD 211) patients were routinely followed-up, representing a significant increase of 3.2 and 3.4 times the respective values before use (U=342.779; P<.001). With respect to the quality of case management, compared to the indicators measured before use, the achievement rate of follow-up examination increased by 50.2%, the achievement rate of the pulmonary function test increased by 26.2%, the awareness rate of chronic respiratory disease knowledge increased by 20.1%, the retention rate increased by 16.3%, and the patient satisfaction rate increased by 9.6% (all P<.001), while the admission rate of acute exacerbation decreased by 42.4% (P<.001) after use of the chronic disease management information system.
UNASSIGNED: Use of a chronic disease management information system improves the quality of chronic respiratory disease case management and reduces the admission rate of patients owing to acute exacerbations of their diseases.

Amyloidosis is a pathological deposition disease that causes a spectrum of organ dysfunction. Pulmonary involvement is generally associated with immunoglobulin light chain type (AL) amyloid. Transthyretin (ATTR) amyloid build up in the lung is thought to be a senile disease observed usually as a finding at autopsy. We describe a case of pulmonary ATTR amyloidosis with concurrent mycobacterial tuberculosis infection.

We aimed to determine the prevalence and outcomes for forced vital capacity percent predicted (FVCpp) decline among patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD). We identified patients with RA-ILD in the Mass General Brigham Healthcare system. RA-ILD diagnosis was determined by review of high-resolution computed tomography (HRCT) imaging by up to three thoracic radiologists. We abstracted FVCpp measurements, covariates, lung transplant, and ILD-related death from the medical record. We employed a relative FVCpp decline cutoff of > 10% within 24 months. We also used a group-based trajectory model to obtain patterns of change from RA-ILD diagnosis. We then assessed for associations of each FVC decline definition with risk of lung transplant or ILD-related death using multivariable logistic regression. We analyzed 172 patients with RA-ILD with a median of 6 FVCpp measurements per patient over 6.5 years of follow-up (mean age 62.2 years, 36% male). There were seven (4%) lung transplants and 44 (26%) ILD-related deaths. Ninety-eight (57%) patients had relative decline of FVCpp by > 10% in 24 months. We identified three trajectory groups of FVCpp change: rapidly declining (n = 24/168 [14%]), slowly declining (n = 90/168 [54%]), and stable/improving (n = 54/168 [32%]). The rapidly declining group and FVCpp > 10% had adjusted odds ratios (aOR) for lung transplant/ILD-related death of 19.2 (95%CI 4.9 to 75.5) and 2.8 (95%CI 1.3 to 6.1) respectively. Over half of patients with RA-ILD had declining FVCpp. The different trajectory patterns demonstrate the importance of FVC monitoring for identifying patients at the highest risk of poor outcomes. Key Points • Over half of patients with RA-ILD had declining FVCpp over a median of 6.5 years of follow-up. • The rapidly declining FVCpp trajectory group had stronger associations with lung transplant and ILD-related death compared to those with FVCpp decline by > 10%. • Clinicians can employ FVC monitoring to proactively treat patients who are at risk of poor outcomes.

Evidence from in vitro studies and observational human disease data suggest the complement system plays a significant role in SARS-CoV-2 pathogenesis, although how complement dysregulation develops in patients with severe COVID-19 is unknown. Here, using a mouse-adapted SARS-CoV-2 virus (SARS2-N501YMA30) and a mouse model of severe COVID-19, we identify significant serologic and pulmonary complement activation following infection. We observed C3 activation in airway and alveolar epithelia, and in pulmonary vascular endothelia. Our evidence suggests that while the alternative pathway is the primary route of complement activation, components of both the alternative and classical pathways are produced locally by respiratory epithelial cells following infection, and increased in primary cultures of human airway epithelia in response to cytokine exposure. This locally generated complement response appears to precede and subsequently drive lung injury and inflammation. Results from this mouse model recapitulate findings in humans, which suggest sex-specific variance in complement activation, with predilection for increased C3 activity in males, a finding that may correlate with more severe disease. Our findings indicate that complement activation is a defining feature of severe COVID-19 in mice and lay the foundation for further investigation into the role of complement in COVID-19.

BACKGROUND: Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea.
OBJECTIVE: The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard.
METHODS: We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device\'s efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant\'s data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods.
RESULTS: The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of -0.25 and 0.33. The RR bias was 0.018, and the LoAs were -1.89 and 1.89.
CONCLUSIONS: Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals.