European Journal of Radiology Open最新文献

{"title":"Advancing radiology with GPT-4: Innovations in clinical applications, patient engagement, research, and learning","authors":"Sadhana Kalidindi ,&nbsp;Janani Baradwaj","doi":"10.1016/j.ejro.2024.100589","DOIUrl":"10.1016/j.ejro.2024.100589","url":null,"abstract":"<div><p>The rapid evolution of artificial intelligence (AI) in healthcare, particularly in radiology, underscores a transformative era marked by a potential for enhanced diagnostic precision, increased patient engagement, and streamlined clinical workflows. Amongst the key developments at the heart of this transformation are Large Language Models like the Generative Pre-trained Transformer 4 (GPT-4), whose integration into radiological practices could potentially herald a significant leap by assisting in the generation and summarization of radiology reports, aiding in differential diagnoses, and recommending evidence-based treatments. This review delves into the multifaceted potential applications of Large Language Models within radiology, using GPT-4 as an example, from improving diagnostic accuracy and reporting efficiency to translating complex medical findings into patient-friendly summaries. The review acknowledges the ethical, privacy, and technical challenges inherent in deploying AI technologies, emphasizing the importance of careful oversight, validation, and adherence to regulatory standards. Through a balanced discourse on the potential and pitfalls of GPT-4 in radiology, the article aims to provide a comprehensive overview of how these models have the potential to reshape the future of radiological services, fostering improvements in patient care, educational methodologies, and clinical research.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100589"},"PeriodicalIF":1.8,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000443/pdfft?md5=c7fde8cd6249665c5a25cde285154c5f&pid=1-s2.0-S2352047724000443-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma","authors":"Hong-Jian Luo ,&nbsp;Jia-Liang Ren ,&nbsp;Li mei Guo ,&nbsp;Jin liang Niu ,&nbsp;Xiao-Li Song","doi":"10.1016/j.ejro.2024.100592","DOIUrl":"10.1016/j.ejro.2024.100592","url":null,"abstract":"<div><h3>Background</h3><p>Human epidermal growth factor receptor 2 (HER2) is a tumor biomarker with significant prognostic and therapeutic implications for invasive ductal breast carcinoma (IDC).</p></div><div><h3>Objective</h3><p>This study aimed to explore the effectiveness of a multisequence magnetic resonance imaging (MRI)-based machine learning radiomics model in classifying the expression status of HER2, including HER2-positive, HER2-low, and HER2 completely negative (HER2-zero), among patients with IDC.</p></div><div><h3>Methods</h3><p>A total of 402 female patients with IDC confirmed through surgical pathology were enrolled and subsequently divided into a training group (n = 250, center I) and a validation group (n = 152, center II). Radiomics features were extracted from the preoperative MRI. A simulated annealing algorithm was used for key feature selection. Two classification tasks were performed: task 1, the classification of HER2-positive vs. HER2-negative (HER2-low and HER2-zero), and task 2, the classification of HER2-low vs. HER2-zero. Logistic regression, random forest (RF), and support vector machine were conducted to establish radiomics models. The performance of the models was evaluated using the area under the curve (AUC) of the operating characteristics (ROC).</p></div><div><h3>Results</h3><p>In total, 4506 radiomics features were extracted from multisequence MRI. A radiomics model for prediction of expression state of HER2 was successfully developed. Among the three classification algorithms, RF achieved the highest performance in classifying HER2-positive from HER2-negative and HER2-low from HER2-zero, with AUC values of 0.777 and 0.731, respectively.</p></div><div><h3>Conclusions</h3><p>Machine learning-based MRI radiomics may aid in the non-invasive prediction of the different expression status of HER2 in IDC.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100592"},"PeriodicalIF":1.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000479/pdfft?md5=6c50536b046e7c7b8b20145494d78138&pid=1-s2.0-S2352047724000479-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peroneus brevis split rupture is underreported on magnetic resonance imaging of the ankle in patients with chronic lateral ankle pain","authors":"Katarzyna Bokwa-Dąbrowska ,&nbsp;Dan Mocanu ,&nbsp;Alex Alexiev ,&nbsp;Katarina Nilsson Helander ,&nbsp;Pawel Szaro","doi":"10.1016/j.ejro.2024.100591","DOIUrl":"10.1016/j.ejro.2024.100591","url":null,"abstract":"<div><h3>Introduction</h3><p>Peroneus brevis split rupture poses a diagnostic challenge, often requiring magnetic resonance imaging (MRI), yet splits are missed in initial radiological reports. However, the frequency of reported peroneus brevis split rupture in clinical MRI examinations is unknown.</p></div><div><h3>Aim</h3><p>This study aimed to investigate underreporting frequency of peroneus brevis split rupture in patients with lateral ankle pain.</p></div><div><h3>Methods</h3><p>We re-evaluated 143 consecutive MRI examinations of the ankle joint, conducted in 2021 in our region, for patients experiencing ankle pain persisting for more than 8 months. Two musculoskeletal radiologists, with 12 and 8 years of experience respectively, assessed the presence of peroneus brevis split rupture. Patients with recent ankle trauma, fractures, postoperative changes, or MRI artifacts were excluded. The radiologists evaluated each MRI for incomplete or complete peroneus brevis split rupture. The consensus between the raters was used as the reference standard. Additionally, raters reviewed the original clinical radiological reports to determine if the presence of peroneus brevis split rupture was noted. Agreement between raters' assessments, consensus, and initial reports was evaluated using Gwet’s AC1 coefficients.</p></div><div><h3>Results</h3><p>Initial radiological reports indicated 23 cases (52.3 %) of peroneus brevis split rupture, meaning 21 cases (47.7 %) were underreported. The Gwet’s AC1 coefficients showed that the agreement between raters and initial reports was 0.401 (standard error 0.070), 95 % CI (0.261, 0.541), p&lt;.001, while the agreement between raters in the study was 0.716 (standard error 0.082), 95 % CI (0.551, 0.881), p&lt;.001.</p></div><div><h3>Conclusion</h3><p>Peroneus brevis split rupture is underreported on MRI scans of patients with lateral ankle pain.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100591"},"PeriodicalIF":1.8,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000467/pdfft?md5=81c4cc1b6e990ec15e6ae90efef8ecf3&pid=1-s2.0-S2352047724000467-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of diffusion-weighted imaging on agreement between radiologists and non-radiologist in musculoskeletal tumor imaging using magnetic resonance","authors":"Gustav Lodeiro ,&nbsp;Katarzyna Bokwa-Dąbrowska ,&nbsp;Andreia Miron ,&nbsp;Pawel Szaro","doi":"10.1016/j.ejro.2024.100590","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100590","url":null,"abstract":"<div><h3>Background</h3><p>Diffusion-weighted imaging (DWI) is widely used in neuroradiology or abdominal imaging but not yet implemented in the diagnosis of musculoskeletal tumors.</p></div><div><h3>Aim</h3><p>This study aimed to evaluate how including diffusion imaging in the MRI protocol for patients with musculoskeletal tumors affects the agreement between radiologists and non-radiologist.</p></div><div><h3>Methods</h3><p>Thirty-nine patients with musculoskeletal tumors (Ewing sarcoma, osteosarcoma, and benign tumors) consulted at our institution were included. Three raters with different experience levels evaluated examinations blinded to all clinical data. The final diagnosis was determined by consensus. MRI examinations were split into 1) conventional sequences and 2) conventional sequences combined with DWI. We evaluated the presence or absence of diffusion restriction, solid nature, necrosis, deep localization, and diameter &gt;4 cm as known radiological markers of malignancy. Agreement between raters was evaluated using Gwet’s AC1 coefficients and interpreted according to Landis and Koch.</p></div><div><h3>Results</h3><p>The lowest agreement was for diffusion restriction in both groups of raters. Agreement among all raters ranged from 0.51 to 0.945, indicating moderate to almost perfect agreement, and 0.772–0.965 among only radiologists indicating substantial to almost perfect agreement.</p></div><div><h3>Conclusion</h3><p>The agreement in evaluating diffusion-weighted MRI sequences was lower than that for conventional MRI sequences, both among radiologists and non-radiologist and among radiologists alone. This indicates that assessing diffusion imaging is more challenging, and experience may impact the agreement.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100590"},"PeriodicalIF":1.8,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000455/pdfft?md5=b3dd9896e7d44191226ac654c59dcb54&pid=1-s2.0-S2352047724000455-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141607342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction of a nomogram combining CEUS and MRI imaging for preoperative diagnosis of microvascular invasion in hepatocellular carcinoma","authors":"Feiqian Wang ,&nbsp;Kazushi Numata ,&nbsp;Akihiro Funaoka ,&nbsp;Takafumi Kumamoto ,&nbsp;Kazuhisa Takeda ,&nbsp;Makoto Chuma ,&nbsp;Akito Nozaki ,&nbsp;Litao Ruan ,&nbsp;Shin Maeda","doi":"10.1016/j.ejro.2024.100587","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100587","url":null,"abstract":"<div><h3>Purpose</h3><p>To use Sonazoid contrast-enhanced ultrasound (S-CEUS) and Gadolinium-Ethoxybenzyl-Diethylenetriamine Penta-Acetic Acid magnetic-resonance imaging (EOB-MRI), exploring a non-invasive preoperative diagnostic strategy for microvascular invasion (MVI) of hepatocellular carcinoma (HCC).</p></div><div><h3>Methods</h3><p>111 newly developed HCC cases were retrospectively collected. Both S-CEUS and EOB-MRI examinations were performed within one month of hepatectomy. The following indicators were investigated: size; vascularity in three phases of S-CEUS; margin, signal intensity, and peritumoral wedge shape in EOB-MRI; tumoral homogeneity, presence and integrity of the tumoral capsule in S-CEUS or EOB-MRI; presence of branching enhancement in S-CEUS; baseline clinical and serological data. The least absolute shrinkage and selection operator regression and multivariate logistic regression analysis were applied to optimize feature selection for the model. A nomogram for MVI was developed and verified by bootstrap resampling.</p></div><div><h3>Results</h3><p>Of the 16 variables we included, wedge and margin in HBP of EOB-MRI, capsule integrity in AP or HBP/PVP images of EOB-MRI/S-CEUS, and branching enhancement in AP of S-CEUS were identified as independent risk factors for MVI and incorporated into construction of the nomogram. The nomogram achieved an excellent diagnostic efficiency with an area under the curve of 0.8434 for full data training set and 0.7925 for bootstrapping validation set for 500 repetitions. In evaluating the nomogram, Hosmer–Lemeshow test for training set exhibited a good model fit with <em>P</em> &gt; 0.05. Decision curve analysis of nomogram model yielded excellent clinical net benefit with a wide range (5–80 % and 85–94 %) of risk threshold.</p></div><div><h3>Conclusions</h3><p>The MVI Nomogram established in this study may provide a strategy for optimizing the preoperative diagnosis of MVI, which in turn may improve the treatment and prognosis of MVI-related HCC.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100587"},"PeriodicalIF":1.8,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235204772400042X/pdfft?md5=ce4e261f345e48950b915bc3a9621dcf&pid=1-s2.0-S235204772400042X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction","authors":"Noriko Nishioka ,&nbsp;Noriyuki Fujima ,&nbsp;Satonori Tsuneta ,&nbsp;Masato Yoshikawa ,&nbsp;Rina Kimura ,&nbsp;Keita Sakamoto ,&nbsp;Fumi Kato ,&nbsp;Haruka Miyata ,&nbsp;Hiroshi Kikuchi ,&nbsp;Ryuji Matsumoto ,&nbsp;Takashige Abe ,&nbsp;Jihun Kwon ,&nbsp;Masami Yoneyama ,&nbsp;Kohsuke Kudo","doi":"10.1016/j.ejro.2024.100588","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100588","url":null,"abstract":"<div><h3>Purpose</h3><p>To evaluate the utility of model-based deep learning reconstruction in prostate diffusion-weighted imaging (DWI).</p></div><div><h3>Methods</h3><p>This retrospective study evaluated two prostate diffusion-weighted imaging (DWI) methods: deep learning reconstruction (DL-DWI) and traditional parallel imaging (PI-DWI). We examined 32 patients with radiologically diagnosed and histologically confirmed prostate cancer (PCa) lesions ≥10 mm. Image quality was evaluated both qualitatively (for overall quality, prostate conspicuity, and lesion conspicuity) and quantitatively, using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) for prostate tissue.</p></div><div><h3>Results</h3><p>In the qualitative evaluation, DL-DWI scored significantly higher than PI-DWI for all three parameters (p&lt;0.0001). In the quantitative analysis, DL-DWI showed significantly higher SNR and CNR values compared to PI-DWI (p&lt;0.0001). Both the prostate tissue and the lesions exhibited significantly higher ADC values in DL-DWI compared to PI-DWI (p&lt;0.0001, p=0.0014, respectively).</p></div><div><h3>Conclusion</h3><p>Model-based DL reconstruction enhanced both qualitative and quantitative aspects of image quality in prostate DWI. However, this study did not include comparisons with other DL-based methods, which is a limitation that warrants future research.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100588"},"PeriodicalIF":1.8,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000431/pdfft?md5=b9b782fb0ca622347ec25b1eacf0e51d&pid=1-s2.0-S2352047724000431-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The association of magnetic resonance imaging features with five molecular subtypes of breast cancer","authors":"Van Thi Nguyen ,&nbsp;Duc Huu Duong ,&nbsp;Quang Thai Nguyen ,&nbsp;Duy Thai Nguyen ,&nbsp;Thi Linh Tran ,&nbsp;Tra Giang Duong","doi":"10.1016/j.ejro.2024.100585","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100585","url":null,"abstract":"<div><h3>Objective</h3><p>To identify the association of magnetic resonance imaging (MRI) features with molecular subtypes of breast cancer (BC).</p></div><div><h3>Materials and methods</h3><p>A retrospective study was conducted on 112 invasive BC patients with preoperative breast MRI. The confirmed diagnosis and molecular subtypes of BC were based on the postoperative specimens. MRI features were collected by experienced radiologists. The association of MRI features of each subtype was compared to other molecular subtypes in univariate and multivariate logistic regression analyses.</p></div><div><h3>Results</h3><p>The proportions of luminal A, luminal B HER2-negative, luminal B HER2-positive, HER2-enriched, and triple-negative BC were 14.3 %, 52.7 %, 12.5 %, 10.7 %, and 9.8 %, respectively. Luminal A was associated with hypo-isointensityon T2-weighted images (OR=6.214, 95 % CI: 1.163–33.215) and non-restricted diffusion on DWI-ADC (OR=6.694, 95 % CI: 1.172–38.235). Luminal B HER2-negative was related to the presence of mass (OR=7.245, 95 % CI: 1.760–29.889) and slow/medium initial enhancement pattern (OR=3.654, 95 % CI: 1.588–8.407). There were no associations between MRI features and luminal B HER2-positive. HER2-enriched tended to present as non-mass enhancement lesions (OR=20.498, 95 % CI: 3.145–133.584) with fast uptake in the initial postcontrast phase (OR=9.788, 95 % CI: 1.689–56.740), and distortion (OR=11.471, 95 % CI: 2.250–58.493). Triple-negative were associated with unifocal (OR=7.877, 95 % CI: 1.180–52.589), hyperintensityon T2-weighted images (OR=14.496, 95 % CI: 1.303–161.328), rim-enhanced lesions (OR=18.706, 95 % CI: 1.915–182.764), and surrounding tissue edema (OR=5.768, 95 % CI: 1.040–31.987).</p></div><div><h3>Conclusion</h3><p>Each molecular subtype of BC has distinct features on breast MRI. These characteristics can serve as an adjunct to immunohistochemistry in diagnosing molecular subtypes, particularly in cases, where traditional methods yield equivocal results.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100585"},"PeriodicalIF":1.8,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000406/pdfft?md5=653b1d0e0dd64eddc9d162db001bb6f2&pid=1-s2.0-S2352047724000406-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-based radiomics combined with clinical features for invasiveness prediction and pathological subtypes classification of subsolid pulmonary nodules","authors":"Miaozhi Liu ,&nbsp;Rui Duan ,&nbsp;Zhifeng Xu ,&nbsp;Zijie Fu ,&nbsp;Zhiheng Li ,&nbsp;Aizhen Pan ,&nbsp;Yan Lin","doi":"10.1016/j.ejro.2024.100584","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100584","url":null,"abstract":"<div><h3>Purpose</h3><p>To construct optimal models for predicting the invasiveness and pathological subtypes of subsolid nodules (SSNs) based on CT radiomics and clinical features.</p></div><div><h3>Materials and Methods</h3><p>This study was a retrospective study involving two centers. A total of 316 patients with 353 SSNs confirmed as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) were included from January 2019 to February 2023. Models based on CT radiomics and clinical features were constructed for classification of AAH/AIS and MIA, MIA and IAC, as well as lepidic-predominant adenocarcinoma (LPA) and acinar-predominant adenocarcinoma (APA). Receiver operating characteristic (ROC) curve was used to evaluate the model performance. Finally, the nomograms based on the optimal models were established.</p></div><div><h3>Results</h3><p>The nomogram based on the combined model (AAH/AIS versus MIA) consisting of lobulation, the GGN-vessel relationship, diameter, CT value, consolidation tumor ratio (CTR) and rad-score performed the best (AUC=0.841), while age, CT value, CTR and rad-score were the significant features for distinguishing MIA from IAC, the nomogram based on these features performed the best (AUC=0.878). There were no significant differences in clinical features between LPA and APA, while the radiomics model based on rad-score showed good performance for distinguishing LPA from APA (AUC=0.926).</p></div><div><h3>Conclusions</h3><p>The nomograms based on radiomics and clinical features could predict the invasiveness of SSNs accurately. Moreover, radiomics models showed good performance in distinguishing LPA from APA.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100584"},"PeriodicalIF":1.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235204772400039X/pdfft?md5=e2c65049a8c3da3633ab27931e354524&pid=1-s2.0-S235204772400039X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation","authors":"Varatharajan Nainamalai ,&nbsp;Hemin Ali Qair ,&nbsp;Egidijus Pelanis ,&nbsp;Håvard Bjørke Jenssen ,&nbsp;Åsmund Avdem Fretland ,&nbsp;Bjørn Edwin ,&nbsp;Ole Jakob Elle ,&nbsp;Ilangko Balasingham","doi":"10.1016/j.ejro.2024.100582","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100582","url":null,"abstract":"<div><h3>Objective</h3><p>Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure a wide variety of unstructured data.</p></div><div><h3>Methods</h3><p>We present a semi-automatic workflow for medical dataset management, including data structuring, research extraction, AI-ground truth creation, and updates. The algorithm creates directories based on keywords in new file names.</p></div><div><h3>Results</h3><p>Our work focuses on organizing computed tomography (CT), magnetic resonance (MR) images, patient clinical data, and segmented annotations. In addition, an AI model is used to generate different initial labels that can be edited manually to create ground truth labels. The manually verified ground truth labels are later included in the structured dataset using an automated algorithm for future research.</p></div><div><h3>Conclusion</h3><p>This is a workflow with an AI model trained on local hospital medical data with output based/adapted to the users and their preferences. The automated algorithms and AI model could be implemented inside a secondary secure environment in the hospital to produce inferences.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100582"},"PeriodicalIF":1.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000376/pdfft?md5=425200236243651a581a0c649773fdeb&pid=1-s2.0-S2352047724000376-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of pulmonary function in COPD patients using dynamic digital radiography: A novel approach utilizing lung signal intensity changes during forced breathing","authors":"Noriaki Wada ,&nbsp;Akinori Tsunomori ,&nbsp;Takeshi Kubo ,&nbsp;Takuya Hino ,&nbsp;Akinori Hata ,&nbsp;Yoshitake Yamada ,&nbsp;Masako Ueyama ,&nbsp;Mizuki Nishino ,&nbsp;Atsuko Kurosaki ,&nbsp;Kousei Ishigami ,&nbsp;Shoji Kudoh ,&nbsp;Hiroto Hatabu","doi":"10.1016/j.ejro.2024.100579","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100579","url":null,"abstract":"<div><h3>Objectives</h3><p>To investigate the association of lung signal intensity changes during forced breathing using dynamic digital radiography (DDR) with pulmonary function and disease severity in patients with chronic obstructive pulmonary disease (COPD).</p></div><div><h3>Methods</h3><p>This retrospective study included 46 healthy subjects and 33 COPD patients who underwent posteroanterior chest DDR examination. We collected raw signal intensity and gray-scale image data. The lung contour was extracted on the gray-scale images using our previously developed automated lung field tracking system and calculated the average of signal intensity values within the extracted lung contour on gray-scale images. Lung signal intensity changes were quantified as SImax/SImin, representing the maximum ratio of the average signal intensity in the inspiratory phase to that in the expiratory phase. We investigated the correlation between SImax/SImin and pulmonary function parameters, and differences in SImax/SImin by disease severity.</p></div><div><h3>Results</h3><p>SImax/SImin showed the highest correlation with VC (r_s = 0.54, P &lt; 0.0001), followed by FEV₁ (r_s = 0.44, P &lt; 0.0001), both of which are key indicators of COPD pathophysiology. In a multivariate linear regression analysis adjusted for confounding factors, SImax/SImin was significantly lower in the severe COPD group compared to the normal group (P = 0.0004) and mild COPD group (P=0.0022), suggesting its potential usefulness in assessing COPD severity.</p></div><div><h3>Conclusion</h3><p>This study suggests that the signal intensity changes of lung fields during forced breathing using DDR reflect the pathophysiology of COPD and can be a useful index in assessing pulmonary function in COPD patients, potentially improving COPD diagnosis and management.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100579"},"PeriodicalIF":1.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000340/pdfft?md5=eedbb14257c44e6f9294f86130763009&pid=1-s2.0-S2352047724000340-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}