Investigation of diaphragmatic motion and projected lung area in diaphragm paralysis patients using dynamic chest radiography.

Background: Dynamic chest radiography (DCR) is a novel and supplementary examination in respiratory diseases. The investigation of other chest diseases using DCR has been explored, identifying a certain correlation of the pulmonary function test (PFT). However, there is a lack of research using DCR parameters to quantitatively evaluate chest disease. The purpose of this study was to investigate the diagnostic value of DCR for diaphragm paralysis (DP). Methods: This retrospective study recruited 118 participants, which include 18 patients with DP, 48 healthy volunteers, and 52 patients with respiratory disease. Comparison of DCR parameters relationships among 3 groups was performed using one-way analysis of variance (ANOVA) and Kruskal-Wallis test. The receiver operating characteristic (ROC) curve was used to compare the value of the DCR parameters to diagnose DP. Results: The differences of excursion of diaphragm (ED) in normal (nb) and forced breathing (fb), ED(fb)-ED(nb), and the parameters of projected lung area (PLA) in inspiratory (ins) and expiratory phase (exp), PLA.exp(fb), PLA.ins(fb)-PLA.ins(nb), and PLA.exp(fb)-PLA.exp(nb) among the 3 groups were statistically significant. The highest area under the curve (AUC) of right-side parameter was the ED(fb)-ED(nb), for which the AUC was 0.8950 [95% confidence interval (CI): 0.7618-1.000], whereas that of the left-side parameter was ED(fb), for which the AUC was 0.9176 [95% confidence interval (CI): 0.8524-0.9829]. Conclusions: The parameters of DCR have good diagnostic value for DP. The highest diagnostic efficiency for DP on the right side is the ED(fb)-ED(nb), with a sensitivity of 95% and a specificity of 78.6%, whereas on the left side is ED(fb), with a sensitivity of 80% and a specificity of 88.2%.

Visual analysis of pulmonary blood flow in pulmonary circulation assessment: differences between two variant algorithms for processing dynamic images.

Background: In the quantitative assessment of pulmonary blood flow, two different processing algorithms [cross-correlation calculation processing (CCC-pro) and reference frame subtraction processing (RFS-pro)] within dynamic imaging systems have been reported to exhibit high correlations with conventional measurement methods. However, reports still need to evaluate these two processing algorithms regarding the different aspects of pulmonary blood flow. This study aimed to analyze the differences in pulmonary circulation. Methods: We conducted a cross-sectional study to evaluate patients with lung cancer who underwent radical surgery, simultaneous dynamic chest radiography (DCR), and pulmonary perfusion scintigraphy (PPS). We assessed the correlation between PPS and two algorithms (CCC-pro and RFS-pro) regarding calculated blood flow ratio (BFR) using Pearson's correlation and linear regression analysis. Additionally, we evaluated consistency using the Bland-Altman analysis. We compared the pulmonary blood flow distributions across six-division lung fields and evaluated each method's blood flow images and histograms of pixel values. Results: From May 2018 to December 2020, we consecutively enrolled 46 patients with lung cancer who met the inclusion criteria (40 male patients, with a mean age of 72.91 years). In these patients, CCC-pro and RFS-pro were correlated (R=0.718, P<0.01); however, CCC-pro was more strongly correlated with PPS than RFS-pro (R=0.859, P<0.01 vs. R=0.549, P<0.01). The Bland-Altman analysis showed high agreement, although systematic errors were observed in relationships other than RFS-pro to PPS. CCC-pro and RFS-pro showed similar blood flow distributions in the upper and lower lung fields, with RFS-pro being dominant in the middle. RFS-pro showed higher pixel values in the hilar region and a histogram shape similar to PPS; however, posture affected the right upper lung field gradient. RFS-pro showed no difference in the BFR when the pulmonary artery region was symmetric; however, potential inaccuracies existed when it overlapped with the cardiovascular shadow. Conclusions: The CCC-pro algorithm was useful for quantifying BFRs, whereas the RFS-pro algorithm accurately evaluated blood flow distribution in lung fields. Further algorithm development is required to enable versatile pulmonary blood flow analysis.

Association between the cardiothoracic ratio on chest X-rays and the respiratory function in patients with interstitial lung diseases: A cross-sectional study.

Objective Patients with advanced interstitial lung disease (ILD) struggle to undergo spirometry to evaluate the respiratory function. The cardiothoracic ratio (CTR) on chest radiography can potentially reflect the lung volume; however, this has not yet been fully established. This study aimed to clarify the relationship between the CTR and the respiratory function in patients with interstitial lung diseases. Patients and Methods We reviewed 120 consecutive patients with idiopathic interstitial lung disease who were admitted to our department between April 2018 and March 2023 and who underwent chest radiography, spirometry, and echocardiography. A multiple linear regression analysis was used to identify the factors associated with the CTR. Correlations between the CTR and the respiratory or cardiac function were assessed using Pearson's correlation coefficient. Results A multiple linear regression analysis showed the percent vital capacity (ß = -0.598, p <0.001), age (ß = 0.405, p <0.001), and female sex (ß = 0.177, p = 0.047) to be independently associated with the CTR, whereas no relationship was observed between the left ventricular ejection fraction, body mass index, and smoking habits. The CTR was significantly negatively correlated with the vital capacity (r = -0.490, p <0.001). Conclusions An increased CTR might reflect a decreased vital capacity, but not a decreased cardiac function, in patients with interstitial lung diseases. Measuring the CTR can thus be beneficial for predicting progression in patients with ILD.

Survey on Findings and Utilization of Preoperative Chest Radiography in Ophthalmic Surgery.

Objective: The objective of this paper is to reconsider the significance of preoperative chest radiography (CXR) before ophthalmic surgery through investigation of imaging findings and usage status. Methods: This retrospective observational clinical study involved 1616 patients who underwent ophthalmic surgery at Saga University Hospital from 1 January 2019 to 31 December 2020. The patients' radiology reports were obtained from the electronic medical records, and their CXR findings, therapeutic interventions, and progress were investigated. Results: Among all patients, 539 (33.4%) had abnormal preoperative CXR findings. Of these patients, 74 (4.6%) had newly identified abnormal findings. In both patient groups, approximately 70% of patients with abnormal findings were aged ≥70 years, and interstitial shadows were the most common finding. Among all patients with abnormal findings, three (0.19%) received preoperative therapeutic interventions, and all surgeries were performed safely. Forty-three patients with abnormal findings were referred to our hospital or other hospitals for further investigation and treatment postoperatively. Among those patients, eight (0.5%) had primary lung cancer, seven underwent surgery, and one received chemoradiation. The other patients were also followed up and received appropriate therapeutic interventions. Conclusions: Before ophthalmic surgery, few patients required actual therapeutic interventions based on their CXR results. However, many abnormal findings were revealed in elderly patients, including some serious diseases. Furthermore, research has suggested that appropriate therapeutic intervention after ophthalmologic surgery may reduce the risk of a poor life prognosis. This study clearly shows that preoperative CXR is not only useful for perioperative systemic management but also ultimately benefits patients. It is also considered particularly meaningful for patients aged ≥70 years.

Role of expiratory chest X-ray in pediatric foreign body aspiration.

PURPOSE: Tracheobronchial foreign body aspiration is a common pediatric emergency and a leading cause of accidental deaths in children. The diagnosis remains sometimes difficult even with physical examination, medical history, and basic X-rays. This challenge necessitates the performance of endoscopy under general anesthesia, regardless of the potential for serious complications. The benefit of strategies like expiratory chest X-rays to reduce unnecessary endoscopies remains uncertain. We evaluated the effectiveness of expiratory chest X-rays in detecting airway foreign bodies to potentially reduce the need for endoscopies. METHODS: We retrospectively studied children with suspected foreign body aspiration who had X-ray and endoscopy. RESULTS: A total of 70 children were included in the study. Out of these, 19 cases (27.1 %) showed pathological findings on standard chest X-rays. However, when expiratory chest X-rays were added, the number of pathological radiographies increased to 37 cases (52.9 %). Out of the 36 foreign bodies that were present, only 2 were not detected. Furthermore, 3 chest X-rays displayed pathological results, while the endoscopies indicated normal findings. Consequently, the overall sensitivity, specificity, positive predictive value, and negative predictive value stood at 94.4 %, 91.1 %, 91.9 %, and 93.9 % respectively. CONCLUSION: The remarkable sensitivity of expiratory chest radiography can eliminate the need for unnecessary endoscopy, but it should be limited to centers lacking access to MDCT. The performance of endoscopy should only be considered when persistent clinical symptoms are observed during auscultation.

Chest Radiograph Screening for Detecting Subclinical Tuberculosis in Asymptomatic Household Contacts, Peru.

The World Health Organization's end TB strategy promotes the use of symptom and chest radiograph screening for tuberculosis (TB) disease. However, asymptomatic early states of TB beyond latent TB infection and active disease can go unrecognized using current screening criteria. We conducted a longitudinal cohort study enrolling household contacts initially free of TB disease and followed them for the occurrence of incident TB over 1 year. Among 1,747 screened contacts, 27 (52%) of the 52 persons in whom TB subsequently developed during follow-up had a baseline abnormal radiograph. Of contacts without TB symptoms, persons with an abnormal radiograph were at higher risk for subsequent TB than persons with an unremarkable radiograph (adjusted hazard ratio 15.62 [95% CI 7.74-31.54]). In young adults, we found a strong linear relationship between radiograph severity and time to TB diagnosis. Our findings suggest chest radiograph screening can extend to detecting early TB states, thereby enabling timely intervention.

A mediastinum-tumour-like pulmonary arteriovenous malformation with association to the pulmonary artery treated with surgical resection.

Despite embolization being now considered the preferred treatment for PAVM, surgical intervention may be considered if the malformation involves large vessels.

Radiographic findings of adenoviral pneumonia in children.

OBJECTIVE: Adenovirus pneumonia is a common cause of community-acquired pneumonia in children and can mimic bacterial pneumonia, but there are few publications on its radiographic features. This study has evaluated the chest radiography findings of community-acquired adenovirus pneumonia in children. The frequency of radiological findings mimicking bacterial pneumonia was investigated. The clinical features of patients with adenovirus pneumonia possessing radiological findings mimicking bacterial pneumonia were also evaluated. MATERIALS AND METHODS: The chest radiographs of patients diagnosed with adenovirus pneumonia were retrospectively reviewed. The chest radiographs were interpreted independently by a pediatric infectious disease specialist and a pediatric radiologist. Chest radiography findings mimicking bacterial pneumonia (bacterial-like) were specified as consolidation +/- pleural effusion. Other findings on chest radiography or a completely normal chest X-ray were specified as findings that were compatible with "typical viral pneumonia". RESULTS: A total of 1407 patients were positive for adenovirus with respiratory multiplex PCR. The 219 patients who met the study criteria were included in the study. Chest radiographs were normal in 58 (26.5 %) patients. The chest radiograph findings mimicked bacterial pneumonia in 41 (18.7 %) patients. CONCLUSION: Adenovirus pneumonia occurs predominantly in children aged five years and younger, as with other viral pneumonias. The radiographic findings in adenovirus pneumonia are predominantly those seen in viral pneumonia. Increasing age and positivity for only adenovirus without other viruses on respiratory multiplex PCR were associated with the chest radiograph being more likely to be "bacterial-like". Adenovirus may lead to lobar/segmental consolidation at a rate that is not very rare.

Text Report Analysis to Identify Opportunities for Optimizing Target Selection for Chest Radiograph Artificial Intelligence Models.

Our goal was to analyze radiology report text for chest radiographs (CXRs) to identify imaging findings that have the most impact on report length and complexity. Identifying these imaging findings can highlight opportunities for designing CXR AI systems which increase radiologist efficiency. We retrospectively analyzed text from 210,025 MIMIC-CXR reports and 168,949 reports from our local institution collected from 2019 to 2022. Fifty-nine categories of imaging finding keywords were extracted from reports using natural language processing (NLP), and their impact on report length was assessed using linear regression with and without LASSO regularization. Regression was also used to assess the impact of additional factors contributing to report length, such as the signing radiologist and use of terms of perception. For modeling CXR report word counts with regression, mean coefficient of determination, R2, was 0.469 ± 0.001 for local reports and 0.354 ± 0.002 for MIMIC-CXR when considering only imaging finding keyword features. Mean R2 was significantly less at 0.067 ± 0.001 for local reports and 0.086 ± 0.002 for MIMIC-CXR, when only considering use of terms of perception. For a combined model for the local report data accounting for the signing radiologist, imaging finding keywords, and terms of perception, the mean R2 was 0.570 ± 0.002. With LASSO, highest value coefficients pertained to endotracheal tubes and pleural drains for local data and masses, nodules, and cavitary and cystic lesions for MIMIC-CXR. Natural language processing and regression analysis of radiology report textual data can highlight imaging targets for AI models which offer opportunities to bolster radiologist efficiency.

The aortic knob index as a novel predictor of new-onset atrial fibrillation after off-pump coronary artery bypass grafting.

PURPOSE: To validate the predictive value of the aortic knob index for identifying new-onset postoperative atrial fibrillation (POAF) after off-pump coronary artery bypass grafting (OPCAB). METHODS: Among 156 patients who underwent isolated OPCAB, 138 consecutive patients without a history of atrial fibrillation were enrolled in this retrospective observational cohort study. The patients were divided into two groups based on the development of POAF. We compared the baseline clinical characteristics; preoperative radiographic characteristics of the aorta, including aortic knob measurements; and perioperative data, between the groups. Logistic regression analysis was performed to identify the predictors of new-onset POAF. RESULTS: New-onset POAF developed in 35 (25.4%) patients. Multivariate logistic regression analysis revealed that the aortic knob index was an independent predictor of POAF and yielded that the risk of POAF increased by 1.85 times when the aortic knob index increased by 0.1 (odds ratio, 1.853; confidence interval, 1.326-2.588; P < 0.001). Receiver operating characteristic analysis revealed that an aortic knob index of 1.364 constituted a cutoff value for new-onset POAF with 80.0% sensitivity and 65.0% specificity. CONCLUSIONS: The aortic knob index on preoperative chest radiography was a significant and independent predictor of new-onset POAF following OPCAB.

Retrospectively assessing evaluation and management of artificial-intelligence detected nodules on uninterpreted chest radiographs in the era of radiologists shortage.

PURPOSE: High volumes of chest radiographs (CXR) remain uninterpreted due to severe shortage of radiologists. These CXRs may be informally reported by non-radiologist physicians, or not reviewed at all. Artificial intelligence (AI) software can aid lung nodule detection. Our aim was to assess evaluation and management by non-radiologists of uninterpreted CXRs with AI detected nodules, compared to retrospective radiology reports. MATERIALS AND METHODS: AI detected nodules on uninterpreted CXRs of adults, performed 30/6/2022-31/1/2023, were evaluated. Excluded were patients with known active malignancy and duplicate CXRs of the same patient. The electronic medical records (EMR) were reviewed, and the clinicians' notes on the CXR and AI detected nodule were documented. Dedicated thoracic radiologists retrospectively interpreted all CXRs, and similarly to the clinicians, they had access to the AI findings, prior imaging and EMR. The radiologists' interpretation served as the ground truth, and determined if the AI-detected nodule was a true lung nodule and if further workup was required. RESULTS: A total of 683 patients met the inclusion criteria. The clinicians commented on 386 (56.5%) CXRs, identified true nodules on 113 CXRs (16.5%), incorrectly mentioned 31 (4.5%) false nodules as real nodules, and did not mention the AI detected nodule on 242 (35%) CXRs, of which 68 (10%) patients were retrospectively referred for further workup by the radiologist. For 297 patients (43.5%) there were no comments regarding the CXR in the EMR. Of these, 77 nodules (11.3%) were retrospectively referred for further workup by the radiologist. CONCLUSION: AI software for lung nodule detection may be insufficient without a formal radiology report, and may lead to over diagnosis or misdiagnosis of nodules.

Effects of body part thickness on low-contrast detail detection and radiation dose during adult chest radiography.

INTRODUCTION: Differences in patient size often provide challenges for radiographers, particularly when choosing the optimum acquisition parameters to obtain radiographs with acceptable image quality (IQ) for diagnosis. This study aimed to assess the effect of body part thickness on IQ in terms of low-contrast detail (LCD) detection and radiation dose when undertaking adult chest radiography (CXR). METHODS: This investigation made use of a contrast detail (CD) phantom. Polymethyl methacrylate (PMMA) was utilised to approximate varied body part thicknesses (9, 11, 15 and 17 cm) simulating underweight, standard, overweight and obese patients, respectively. Different tube potentials were tested against a fixed 180 cm source to image distance (SID) and automatic exposure control (AEC). IQ was analysed using bespoke software thus providing an image quality figure inverse (IQFinv ) value which represents LCD detectability. Dose area product (DAP) was utilised to represent the radiation dose. RESULTS: IQFinv values decreased statistically (P = 0.0001) with increasing phantom size across all tube potentials studied. The highest IQFinv values were obtained at 80 kVp for all phantom thicknesses (2.29, 2.02, 1.8 and 1.65, respectively). Radiation dose increased statistically (P = 0.0001) again with increasing phantom thicknesses. CONCLUSION: Our findings demonstrate that lower tube potentials provide the highest IQFinv scores for various body part thicknesses. This is not consistent with professional practice because radiographers frequently raise the tube potential with increased part thickness. Higher tube potentials did result in radiation dose reductions. Establishing a balance between dose and IQ, which must be acceptable for diagnosis, can prevent the patient from receiving unnecessary additional radiation dose.

Refined scan protocol for the evaluation of pulmonary perfusion standardized image quality and reduced radiation dose in dynamic chest radiography.

PURPOSE: Dynamic chest radiography (DCR) is a novel imaging technique used to noninvasively evaluate pulmonary perfusion. However, the standard DCR protocol, which is roughly adapted to the patient's body size, occasionally causes over- or underexposure, which could influence clinical evaluation. Therefore, we proposed a refined protocol by increasing the number of patient body mass index (BMI) categories from three to seven groups and verified its usefulness by comparing the image sensitivity indicators (S-values) and entrance surface doses (ESDs) of the conventional protocol with those of our refined protocol. METHODS: This retrospective observational study included 388 datasets (standing position, 224; supine position, 164) for the conventional protocol (December 2019-April 2021) and 336 datasets (standing position, 233; supine position, 103) for the refined protocol (June-November 2021). The conventional protocol (BMI-3 protocol) divided the patients into three BMI groups (BMI < 17, 17≤BMI < 25, and BMI ≥ 25 kg/m2 ), whereas the refined protocol (BMI-7 protocol) divided the patients into seven BMI groups (BMI < 17, 17 ≤ BMI < 20, 20 ≤ BMI < 23, 23 ≤ BMI < 26, 26 ≤ BMI < 29, 29 ≤ BMI < 32, and BMI ≥ 32 kg/m2 ). The coefficients of variation (CVs) for the S-values and ESDs acquired using the two protocols were compared. RESULTS: The CVs of the S-values in the BMI-7 protocol group were significantly lower than those in the BMI-3 protocol group for the standing (28.8% vs. 16.7%; p < 0.01) and supine (24.5% vs. 17.7%; p < 0.01) positions. The ESDs of patients scanned using the BMI-7 protocol were significantly lower than those scanned using the BMI-3 protocol in the standing (1.3 vs. 1.1 mGy; p < 0.01) and supine positions (2.5 vs. 1.6 mGy; p < 0.01), although the mean BMI of the two groups were similar. CONCLUSION: We introduced the BMI-7 protocol and demonstrated its standardized image quality and reduced radiation exposure in patients undergoing DCR.

[The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. / Apport de l'intelligence artificielle dans le post-traitement de l'imagerie thoracique.

The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.

The role of chest X-ray in the early diagnosis and staging of sarcoidosis: Is it really should be done?

BACKGROUND: Sarcoidosis is a chronic granulomatous disease characterized by non-caseating granuloma. The conventional chest X-ray (CXR) has important role in the diagnosis, staging and follow-up of disease. Computed tomography (CT) is a second-line imaging method used to determine the extent, complications and differential diagnosis of sarcoidosis. OBJECTIVES: To determine the role of CXR in the early diagnosis and staging of sarcoidosis and to compare with CT imaging. METHODS: One hundred and nine sarcoidosis patients followed at a single center were included in the study. Demographic, radiological, and clinical data of 81 patients were obtained from a total of 109 patients, and the record data of these 81 patients were evaluated. Patients who could not be reached for all tests were excluded from the study. CXR and CT imaging taken at diagnosis were evaluated retrospectively independently from two radiologists and one rheumatologist. RESULTS: Among 109 patients, eighty-one patients CXR and CT imaging taken at the same center has been reached. Among 81 sarcoidosis patients 23 (28.4%) were male, 58 (71.6%) were female. The mean patients age was 46.4 years and the mean disease duration was 3.8 years. CXR is regarded as normal at diagnosis in 30 patients (37%), while all of these patients had findings consistent with sarcoidosis on CT imaging. CT imaging are more superior than CXR in the early diagnosis and staging of sarcoidosis (p=0.001). Also CT imaging is more superior for detection of disease extent and complications. CONCLUSIONS: In this study, we observed that CT imaging outperforms CXR in terms of early detection and staging of sarcoidosis. The use of CT imaging is important for early diagnosis and staging of sarcoidosis. The low performance of CXR is a condition that requires the discussion of this method. Multicenter prospective study is needed in this regard.

Optimal conversion coefficient from easily measurable dose to effective dose with consideration to radiation quality for posterior-anterior chest radiography.

Effective dose is sometimes used to compare medical radiation exposure to patients and natural radiation for providing explanations about radiation exposure to patients, but its calculation is lengthy and requires dedicated measuring devices. The purpose of this study was to identify the most suitable conversion coefficient for conversion of easily measurable dose to effective dose in posterior-anterior chest radiography, and to evaluate its accuracy by direct measurement. We constructed an examination environment using Monte Carlo simulation, and evaluated the variation in conversion coefficients from incident air kerma (IAK), entrance-surface air kerma (ESAK), and air kerma-area product (KAP) to effective dose when the irradiation field size and radiation quality were changed. Effective doses were also measured directly using thermoluminescence dosimeters and compared with the effective dose obtained from conversion coefficients. The KAP conversion coefficient most effectively suppressed the effect of irradiation field size, and was then used to set conversion coefficients for various half-value layers. The optimal conversion coefficient was 0.00023 [mSv/(mGy·cm2)] at 120 kVp (half-value layer = 5.5 mmAl). Evaluation of the direct measurements obtained with various radiation qualities revealed that the accuracy of the conversion coefficient was maintained at ≤ 11%. The proposed conversion coefficient can be easily calculated even in facilities that do not have equipment for measuring effective dose, and might enable the use of effective dose for providing explanations about radiation exposure to patients.

Relationship between pulmonary blood flow and volume following lung resection using dynamic perfusion digital radiography.

Background: Surgical intervention for lung resection can cause ventilation-perfusion mismatches and affect gas exchange; however, minimally invasive assessment of blood flow is difficult. This study aimed to evaluate changes in pulmonary blood flow after radical lung cancer surgery using a minimally invasive dynamic digital chest radiography system. Methods: We evaluated 64 patients who underwent radical lobectomies. Postoperative changes in pulmonary blood flow, assessed using dynamic chest radiography-based blood flow ratios (BFRs), were compared with the temporal evolution of both functional lung volumes (FLVs) and estimated lung weight (ELW) derived from computed tomography (CT) volumetry. Results: FLVs on the affected side gradually recovered over time from the lowest value observed 3 months after surgery in all procedures. BFRs on the affected side also showed a gradual recovery from the lowest value 1 month after surgery, except for left upper lobectomies (LULs). In LULs, FLVs and ELWs increased proportionally up to 3 months after surgery, with lung volumes continuing to increase thereafter. The recovery of BFRs differed depending on the resected lobe. Conclusions: A relationship between pulmonary blood flow and FLV was observed in the postoperative period. Despite varying compensatory responses depending on the surgical procedure, FLV recovery coincided with increased pulmonary blood flow.

Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography.

BACKGROUND: In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis. METHODS: We analyzed chest X-ray images obtained from a health examination center in Fukushima and the National Institutes of Health (NIH) Chest X-ray 14 dataset. We categorized these data into two types: type A included both Fukushima and NIH datasets, and type B included only the Fukushima dataset. We also demonstrated pulmonary nodules in the form of a heatmap display on each chest radiograph and calculated the positive probability score as an index value. RESULTS: Our novel AI algorithms had a receiver operating characteristic (ROC) area under the curve (AUC) of 0.74, a sensitivity of 0.75, and a specificity of 0.60 for the type A dataset. For the type B dataset, the respective values were 0.79, 0.72, and 0.74. The algorithms in both the type A and B datasets were superior to the accuracy of radiologists and similar to previous studies. CONCLUSIONS: The proprietary AI algorithms had a similar accuracy for interpreting chest radiographs when compared with previous studies and radiologists. Especially, we could train a high quality AI algorithm, even with our small type B data set. However, further studies are needed to improve and further validate the accuracy of our AI algorithm.