Ultrasonic treatment of endocrine disrupting compounds, pharmaceuticals, and personal care products in water: An updated review.

Pharmaceuticals, personal care products (PPCPs), and endocrine-disrupting compounds (EDCs) have seen a recent sustained increase in usage, leading to increasing discharge and accumulation in wastewater. Conventional water treatment and disinfection processes are somewhat limited in effectively addressing this micropollutant issue. Ultrasonication (US), which serves as an advanced oxidation process, is based on the principle of ultrasound irradiation, exposing water to high-frequency waves, inducing thermal decomposition of H2O while using the produced radicals to oxidize and break down dissolved contaminants. This review evaluates research over the past five years on US-based technologies for the effective degradation of EDCs and PPCPs in water and assesses various factors that can influence the removal rate: solution pH, temperature of water, presence of background common ions, natural organic matter, species that serve as promoters and scavengers, and variations in US conditions (e.g., frequency, power density, and reaction type). This review also discusses various types of carbon/non-carbon catalysts, O3 and ultraviolet processes that can further enhance the degradation efficiency of EDCs and PPCPs in combination with US processes. Furthermore, numerous types of EDCs and PPCPs and recent research trends for these organic contaminants are considered.

Removal of endocrine disruptors and pharmaceuticals by graphene oxide-based membranes in water: A review.

Membrane-based water treatment has emerged as a promising solution to address global water challenges. Graphene oxide (GO) has been successfully employed in membrane filtration processes owing to its reversible properties, large-scale production potential, layer-to-layer stacking, great oxygen-based functional groups, and unique physicochemical characteristics, including the creation of nano-channels. This review evaluates the separation performance of various GO-based membranes, manufactured by coating or interfacial polymerization with different support layers such as polymer, metal, and ceramic, for endocrine-disrupting compounds (EDCs) and pharmaceutically active compounds (PhACs). In most studies, the addition of GO significantly improved the removal efficiency, flux, porosity, hydrophilicity, stability, mechanical strength, and antifouling performance compared to pristine membranes. The key mechanisms involved in contaminant removal included size exclusion, electrostatic exclusion, and adsorption. These mechanisms could be ascribed to the physicochemical properties of compounds, such as molecular size and shape, hydrophilicity, and charge state. Therefore, understanding the removal mechanisms based on compound characteristics and appropriately adjusting the operational conditions are crucial keys to membrane separation. Future research directions should explore the characteristics of the combination of GO derivatives with various support layers, by tailoring diverse operating conditions and compounds for effective removal of EDCs and PhACs. This is expected to accelerate the development of surface modification strategies for enhanced contaminant removal.

2023 Survey on User Experience of Artificial Intelligence Software in Radiology by the Korean Society of Radiology.

OBJECTIVE: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). MATERIALS AND METHODS: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. RESULTS: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. CONCLUSION: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

Fabrication of MoS2 restrained magnetic chitosan polysaccharide composite for the photocatalytic degradation of organic dyes.

Chitosan (CS) polysaccharide is expected to exhibit greater ionic conductivity, which can be attributed to its increased amino group content when it is blended with different semiconducting materials. Herein, the work used this conducting ability of chitosan and prepared a heterogeneous MoS2-induced magnetic chitosan (MF@CS) composite via the co-precipitation method, which was used to scrutinize the catalytic performance with Methylene Blue (MB) and Malachite Green (MG) dyes by visible light irradiation. The saturation magnetization value of the MF@CS composite is found to be 7.8 emu/g, which is less when compared to that of pristine Fe3O4 (55.7 emu/g) particles. The bandgap of the MF@CS composite is âˆ¼ 2.17eV, which exceeds the bandgap (Eg) of bare MoS2 of 1.80 eV. The maximum color removal of 96.3 % and 93.4 % for MB and MG dyestuffs is recognized in the exposure of the visible spectrum, respectively. At a starting dye dosage of 30 mg/L, 0.1 g/L of MF@CS, a pH level of 8-11, and 70 min of contact with direct light. The photocatalyst provides extremely good durability for a maximum of five phases. Hence, the MF@CS matrix is a viable and appropriate substance for the efficient treatment of effluents containing dye molecules.

Photocatalytic and electrocatalytic degradation of bisphenol A in the presence of graphene/graphene oxide-based nanocatalysts: A review.

Bisphenol A (BPA), a widely recognized endocrine disrupting compound, has been discovered in drinking water sources/finished water and domestic wastewater influent/effluent. Numerous studies have shown photocatalytic and electrocatalytic oxidation to be very effective for the removal of BPA, particularly in the addition of graphene/graphene oxide (GO)-based nanocatalysts. Nevertheless, the photocatalytic and electrocatalytic degradation of BPA in aqueous solutions has not been reviewed. Therefore, this review gives a comprehensive understanding of BPA degradation during photo-/electro-catalytic activity in the presence of graphene/GO-based nanocatalysts. Herein, this review evaluated the main photo-/electro-catalytic degradation mechanisms and pathways for BPA removal under various water quality/chemistry conditions (pH, background ions, natural organic matter, promotors, and scavengers), the physicochemical characteristics of various graphene/GO-based nanocatalysts, and various operating conditions (voltage and current). Additionally, the reusability/stability of graphene/GO-based nanocatalysts, hybrid systems combined with ozone/ultrasonic/Fenton oxidation, and prospective research areas are briefly described.

Ultrasonic treatment of dye chemicals in wastewater: A review.

The existence of pollutants, such as toxic organic dye chemicals, in water and wastewater raises concerns as they are inadequately eliminated through conventional water and wastewater treatment methods, including physicochemical and biological processes. Ultrasonic treatment has emerged as an advanced treatment process that has been widely applied to the decomposition of recalcitrant organic contaminants. Ultrasonic treatment has several advantages, including easy operation, sustainability, non-secondary pollutant production, and saving energy. This review examines the elimination of dye chemicals and categorizes them into cationic and anionic dyes based on the existing literature. The objectives include (i) analyzing the primary factors (water quality and ultrasonic conditions) that influence the sonodegradation of dye chemicals and their byproducts during ultrasonication, (ii) assessing the impact of the different sonocatalysts and combined systems (with ozone and ultraviolet) on sonodegradation, and (iii) exploring the characteristics-based removal mechanisms of dyes. In addition, this review proposes areas for future research on ultrasonic treatment of dye chemicals in water and wastewater.

Effect of smoking on the diagnostic results and complication rates of percutaneous transthoracic needle biopsy.

OBJECTIVE: To investigate the association of smoking with the outcomes of percutaneous transthoracic needle biopsy (PTNB). METHODS: In total, 4668 PTNBs for pulmonary lesions were retrospectively identified. The associations of smoking status (never, former, current smokers) and smoking intensity (≤ 20, 21-40, > 40 pack-years) with diagnostic results (malignancy, non-diagnostic pathologies, and false-negative results in non-diagnostic pathologies) and complications (pneumothorax and hemoptysis) were assessed using multivariable logistic regression analysis. RESULTS: Among the 4668 PTNBs (median age of the patients, 66 years [interquartile range, 58-74]; 2715 men), malignancies, non-diagnostic pathologies, and specific benign pathologies were identified in 3054 (65.4%), 1282 (27.5%), and 332 PTNBs (7.1%), respectively. False-negative results for malignancy occurred in 20.5% (236/1153) of non-diagnostic pathologies with decidable reference standards. Current smoking was associated with malignancy (adjusted odds ratio [OR], 1.31; 95% confidence interval [CI]: 1.02-1.69; p = 0.03) and false-negative results (OR, 2.64; 95% CI: 1.32-5.28; p = 0.006), while heavy smoking (> 40 pack-years) was associated with non-diagnostic pathologies (OR, 1.69; 95% CI: 1.19-2.40; p = 0.003) and false-negative results (OR, 2.12; 95% CI: 1.17-3.92; p = 0.02). Pneumothorax and hemoptysis occurred in 21.8% (1018/4668) and 10.6% (495/4668) of PTNBs, respectively. Heavy smoking was associated with pneumothorax (OR, 1.33; 95% CI: 1.01-1.74; p = 0.04), while heavy smoking (OR, 0.64; 95% CI: 0.40-0.99; p = 0.048) and current smoking (OR, 0.64; 95% CI: 0.42-0.96; p = 0.04) were inversely associated with hemoptysis. CONCLUSION: Smoking history was associated with the outcomes of PTNBs. Current and heavy smoking increased false-negative results and changed the complication rates of PTNBs. CLINICAL RELEVANCE STATEMENT: Smoking status and intensity were independently associated with the outcomes of PTNBs. Non-diagnostic pathologies should be interpreted cautiously in current or heavy smokers. A patient's smoking history should be ascertained before PTNB to predict and manage complications. KEY POINTS: • Smoking status and intensity might independently contribute to the diagnostic results and complications of PTNBs. • Current and heavy smoking (> 40 pack-years) were independently associated with the outcomes of PTNBs. • Operators need to recognize the association between smoking history and the outcomes of PTNBs.

All-solid-state Z-scheme ZnFe-LDH/rGO/g-C3N5 heterojunction for enhanced sonophotocatalytic degradation of ciprofloxacin: Performance and mechanistic insights.

The fabrication of all-solid-state Z-scheme sonophotocatalysts is vital for improving the transfer rate of photogenerated electrons to remove antibiotics present in wastewater. Herein, a novel indirect Z-scheme ZnFe-layered double hydroxide (LDH)/reduced graphene oxide (rGO)/graphitic carbon nitride (g-C3N5) heterojunction was synthesized using a simple strategy. The ZnFe-LDH/rGO/g-C3N5 (ZF@rGCN) ternary composites were systematically characterized using different techniques. Results revealed that the 15%ZF@rGCN catalyst achieved a ciprofloxacin (CIP) degradation efficiency of 95% via the synergistic effect of sonocatalysis and photocatalysis. The improved sonophotocatalytic performance of the ZF@rGCN heterojunction was attributed to an increase in the number of active sites, a Z-scheme charge-transfer channel in ZF@rGCN, and an extended visible light response range. The introduction of rGO further enhanced the charge-transfer rate and preserved the reductive and oxidative sites of the ZF@rGCN system, thereby affording additional reactive species to participate in CIP removal. In addition, owing to its unique properties, rGO possibly increased the absorption of incident light and served as an electronic bridge in the as-formed ZF@rGCN catalyst. Finally, the possible CIP degradation pathways and the sonophotocatalytic Z-scheme charge-migration route of ZF@rGCN were proposed. This study presents a new approach for fabricating highly efficient Z-scheme sonophotocatalysts for environmental remediation.

Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.

OBJECTIVES: To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data. METHODS: An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127 030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present. RESULTS: The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269 ± 1015 vs. 9429 ± 1057; P = .02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (Ps < .01). The radiologists' pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46/51] vs. 96.1% [49/51]; P = .19), nodule (90.0% [54/60] vs. 85.0% [51/60]; P = .26), consolidation (100% [54/54] vs. 96.3% [52/54]; P = .50), and GGO (91.7% [44/48] vs. 95.8% [46/48]; P = .69). CONCLUSIONS: SR-reconstructed chest radiographs using 64-fold reduced data showed a lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities. ADVANCES IN KNOWLEDGE: This is the first study applying super-resolution in data reduction of chest radiographs.

Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions.

BACKGROUND. Timely and accurate interpretation of chest radiographs obtained to evaluate endotracheal tube (ETT) position is important for facilitating prompt adjustment if needed. OBJECTIVE. The purpose of our study was to evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) system for detecting ETT presence and position on chest radiographs in three patient samples from two different institutions. METHODS. This retrospective study included 539 chest radiographs obtained immediately after ETT insertion from January 1 to March 31, 2020, in 505 patients (293 men, 212 women; mean age, 63 years) from institution A (sample A); 637 chest radiographs obtained from January 1 to January 3, 2020, in 302 patients (157 men, 145 women; mean age, 66 years) in the ICU (with or without an ETT) from institution A (sample B); and 546 chest radiographs obtained from January 1 to January 20, 2020, in 83 patients (54 men, 29 women; mean age, 70 years) in the ICU (with or without an ETT) from institution B (sample C). A commercial DL-based AI system was used to identify ETT presence and measure ETT tip-to-carina distance (TCD). The reference standard for proper ETT position was TCD between greater than 3 cm and less than 7 cm, determined by human readers. Critical ETT position was separately defined as ETT tip below the carina or TCD of 1 cm or less. ROC analysis was performed. RESULTS. AI had sensitivity and specificity for identification of ETT presence of 100.0% and 98.7% (sample B) and 99.2% and 94.5% (sample C). AI had sensitivity and specificity for identification of improper ETT position of 72.5% and 92.0% (sample A), 78.9% and 100.0% (sample B), and 83.7% and 99.1% (sample C). At a threshold y-axis TCD of 2 cm or less, AI had sensitivity and specificity for critical ETT position of 100.0% and 96.7% (sample A), 100.0% and 100.0% (sample B), and 100.0% and 99.2% (sample C). CONCLUSION. AI identified improperly positioned ETTs on chest radiographs obtained after ETT insertion as well as on chest radiographs obtained of patients in the ICU at two institutions. CLINICAL IMPACT. Automated AI identification of improper ETT position on chest radiographs may allow earlier repositioning and thereby reduce complications.

Usual Interstitial Pneumonia: Associations With Complications After Percutaneous Transthoracic Needle Lung Biopsy.

BACKGROUND. Changes in lung parenchyma elasticity in usual interstitial pneumonia (UIP) may increase the risk for complications after percutaneous transthoracic needle biopsy (PTNB) of the lung. OBJECTIVE. The purpose of this article was to investigate the association of UIP findings on CT with complications after PTNB, including pneumothorax, pneumothorax requiring chest tube insertion, and hemoptysis. METHODS. This retrospective single-center study included 4187 patients (mean age, 63.8 ± 11.9 [SD] years; 2513 men, 1674 women) who underwent PTNB between January 2010 and December 2015. Patients were categorized into a UIP group and non-UIP group by review of preprocedural CT. In the UIP group, procedural CT images were reviewed to assess for traversal of UIP findings by needle. Multivariable logistic regression analyses were performed to identify associations between the UIP group and needle traversal with postbiopsy complications, controlling for a range of patient, lesion, and procedural characteristics. RESULTS. The UIP and non-UIP groups included 148 and 4039 patients, respectively; in the UIP group, traversal of UIP findings by needle was observed in 53 patients and not observed in 95 patients. The UIP group, in comparison with the non-UIP group, had a higher frequency of pneumothorax (35.1% vs 17.9%, p < .001) and pneumothorax requiring chest tube placement (6.1% vs 1.5%, p = .001) and lower frequency of hemoptysis (2.0% vs 6.1%, p = .03). In multivariable analyses, the UIP group with traversal of UIP findings by needle, relative to the non-UIP group, showed independent associations with pneumothorax (OR, 5.25; 95% CI, 2.94-9.37; p < .001) and pneumothorax requiring chest tube placement (OR, 9.55; 95% CI, 3.74-24.38; p < .001). The UIP group without traversal of UIP findings by needle, relative to the non-UIP group, was not independently associated with pneumothorax (OR, 1.18; 95% CI, 0.71-1.97; p = .51) or pneumothorax requiring chest tube placement (OR, 1.08; 95% CI, 0.25-4.72; p = .92). The UIP group, with or without traversal of UIP findings by needle, was not independently associated with hemoptysis. No patient experienced air embolism or procedure-related death. CONCLUSION. Needle traversal of UIP findings is a risk factor for pneumothorax and pneumothorax requiring chest tube placement after PTNB. CLINICAL IMPACT. When performing PTNB in patients with UIP, radiologists should plan a needle trajectory that does not traverse UIP findings, when possible.

Photocatalytic boron nitride-based nanomaterials for the removal of selected organic and inorganic contaminants in aqueous solution: A review.

Boron nitride (BN) coupled with various conventional and advanced photocatalysts has been demonstrated to exhibit extraordinary activity for photocatalytic degradation because of its unique properties, including a high surface area, constant wide-bandgap semiconducting property, high thermal-oxidation resistance, good hydrogen-adsorption performance, and high chemical/mechanical stability. However, only limited reviews have discussed the application of BN or BN-based nanomaterials as innovative photocatalysts, and it does not cover the recent results and the developments on the application of BN-based nanomaterials for water purification. Herein, we present a complete review of the present findings on the photocatalytic degradation of different contaminants by various BN-based nanomaterials. This review includes the following: (i) the degradation behavior of different BN-based photocatalysts for various contaminants, such as selected dye compounds, pharmaceuticals, personal care products, pesticides, and inorganics; (ii) the stability/reusability of BN-based photocatalysts; and (iii) brief discussion for research areas/future studies on BN-based photocatalysts.

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.

OBJECTIVES: 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. CLINICAL RELEVANCE STATEMENT: Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity. KEY POINTS: • 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.

Cigarette smoking and disproportionate changes of thoracic skeletal muscles in low-dose chest computed tomography.

Association between smoking intensity and the quantity and quality of thoracic skeletal muscles (TSMs) remains unexplored. Skeletal muscle index (SMI; skeletal muscle area/height2) and percentage of normal attenuation muscle area (NAMA%) were measured to represent the quantity and quality of the skeletal muscles, respectively, and quantification was performed in pectoralis muscle at aortic arch (AA-PM), TSM at carina (C-TSM), erector spinae muscle at T12 (T12-ESM), and skeletal muscle at L1 (L1-SM). Among the 258 men (median age, 62 years [IQR: 58-69]), 183 were current smokers (median smoking intensity, 40 pack-years [IQR: 30-46]). SMI and NAMA% of AA-PM significantly decreased with pack-year (ß = - 0.028 and - 0.076; P < 0.001 and P = 0.021, respectively). Smoking intensity was inversely associated with NAMA% of C-TSM (ß = - 0.063; P = 0.001), whereas smoking intensity showed a borderline association with SMI of C-TSM (ß = - 0.023; P = 0.057). Smoking intensity was associated with the change in NAMA% of L1-SM (ß = - 0.040; P = 0.027), but was not associated with SMI of L1-SM (P > 0.05). Neither NAMA% nor SMI of T12-ESM was affected by smoking intensity (P > 0.05). In conclusion, smoking intensity was associated with the change of TSMs. Its association varied according to the location of TSMs, with the most associated parts being the upper (AA-PM) and middle TSMs (C-TSM).

The Performance of a Deep Learning-Based Automatic Measurement Model for Measuring the Cardiothoracic Ratio on Chest Radiographs.

OBJECTIVE: Prior studies on models based on deep learning (DL) and measuring the cardiothoracic ratio (CTR) on chest radiographs have lacked rigorous agreement analyses with radiologists or reader tests. We validated the performance of a commercially available DL-based CTR measurement model with various thoracic pathologies, and performed agreement analyses with thoracic radiologists and reader tests using a probabilistic-based reference. MATERIALS AND METHODS: This study included 160 posteroanterior view chest radiographs (no lung or pleural abnormalities, pneumothorax, pleural effusion, consolidation, and n = 40 in each category) to externally test a DL-based CTR measurement model. To assess the agreement between the model and experts, intraclass or interclass correlation coefficients (ICCs) were compared between the model and two thoracic radiologists. In the reader tests with a probabilistic-based reference standard (Dawid-Skene consensus), we compared diagnostic measures-including sensitivity and negative predictive value (NPV)-for cardiomegaly between the model and five other radiologists using the non-inferiority test. RESULTS: For the 160 chest radiographs, the model measured a median CTR of 0.521 (interquartile range, 0.446-0.59) and a mean CTR of 0.522 ± 0.095. The ICC between the two thoracic radiologists and between the model and two thoracic radiologists was not significantly different (0.972 versus 0.959, p = 0.192), even across various pathologies (all p-values > 0.05). The model showed non-inferior diagnostic performance, including sensitivity (96.3% versus 97.8%) and NPV (95.6% versus 97.4%) (p < 0.001 in both), compared with the radiologists for all 160 chest radiographs. However, it showed inferior sensitivity in chest radiographs with consolidation (95.5% versus 99.9%; p = 0.082) and NPV in chest radiographs with pleural effusion (92.9% versus 94.6%; p = 0.079) and consolidation (94.1% versus 98.7%; p = 0.173). CONCLUSION: While the sensitivity and NPV of this model for diagnosing cardiomegaly in chest radiographs with consolidation or pleural effusion were not as high as those of the radiologists, it demonstrated good agreement with the thoracic radiologists in measuring the CTR across various pathologies.

Erratum: Correction of Affiliations in the Article "Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019".

This corrects the article on p. e413 in vol. 35, PMID: 33258333.

Effects of Implementing Artificial Intelligence-Based Computer-Aided Detection for Chest Radiographs in Daily Practice on the Rate of Referral to Chest Computed Tomography in Pulmonology Outpatient Clinic.

OBJECTIVE: The clinical impact of artificial intelligence-based computer-aided detection (AI-CAD) beyond diagnostic accuracy remains uncertain. We aimed to investigate the influence of the clinical implementation of AI-CAD for chest radiograph (CR) interpretation in daily practice on the rate of referral for chest computed tomography (CT). MATERIALS AND METHODS: AI-CAD was implemented in clinical practice at the Seoul National University Hospital. CRs obtained from patients who visited the pulmonology outpatient clinics before (January-December 2019) and after (January-December 2020) implementation were included in this study. After implementation, the referring pulmonologist requested CRs with or without AI-CAD analysis. We conducted multivariable logistic regression analyses to evaluate the associations between using AI-CAD and the following study outcomes: the rate of chest CT referral, defined as request and actual acquisition of chest CT within 30 days after CR acquisition, and the CT referral rates separately for subsequent positive and negative CT results. Multivariable analyses included various covariates such as patient age and sex, time of CR acquisition (before versus after AI-CAD implementation), referring pulmonologist, nature of the CR examination (baseline versus follow-up examination), and radiology reports presence at the time of the pulmonology visit. RESULTS: A total of 28546 CRs from 14565 patients (mean age: 67 years; 7130 males) and 25888 CRs from 12929 patients (mean age: 67 years; 6435 males) before and after AI-CAD implementation were included. The use of AI-CAD was independently associated with increased chest CT referrals (odds ratio [OR], 1.33; P = 0.008) and referrals with subsequent negative chest CT results (OR, 1.46; P = 0.005). Meanwhile, referrals with positive chest CT results were not significantly associated with AI-CAD use (OR, 1.08; P = 0.647). CONCLUSION: The use of AI-CAD for CR interpretation in pulmonology outpatients was independently associated with an increased frequency of overall referrals for chest CT scans and referrals with subsequent negative results.

Correction: Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/42717.].

Tumor-associated prognostic factors extractable from chest CT scans in patients with lung cancer.

Accurately predicting the prognosis of patients with lung cancer before or at the time of treatment would offer clinicians an opportunity to tailor management plans more precisely to individual patients. Considering that chest computed tomography (CT) scans are universally acquired in patients with lung cancer for clinical staging or response evaluation, fully extracting and utilizing the prognostic information embedded in this modality would be a reasonable approach. Herein, we review tumor-related prognostic factors that are extractable from CT scans, including the tumor dimensions, presence of ground-glass opacity (GGO), margin characteristics, tumor location, and deep learning-based features. Tumor dimensions include diameter and volume, which are among the most potent prognostic factors in lung cancer. In lung adenocarcinomas, the solid component size on CT scans as well as the total tumor size is associated with the prognosis. The areas of GGO indicate the lepidic component and are associated with better postoperative survival in early-stage lung adenocarcinomas. As for the margin characteristics, which represent the CT manifestation of fibrotic stroma or desmoplasia, tumor spiculation should be evaluated. The tumor location in the central lung is associated with occult nodal metastasis and is a worse prognostic factor per se. Last but not least, deep learning analysis enables prognostic feature extraction beyond the human eyes.