Intelligence-based medicine最新文献

{"title":"Cognitive behavioral therapy for chronic pain supported by digital patient feedback and artificial intelligence: Do patients with socioeconomic risk factors benefit?","authors":"John D. Piette ,&nbsp;Mary A. Driscoll ,&nbsp;Eugenia Buta ,&nbsp;Robert D. Kerns ,&nbsp;Alicia A. Heapy","doi":"10.1016/j.ibmed.2024.100164","DOIUrl":"10.1016/j.ibmed.2024.100164","url":null,"abstract":"<div><h3>Background</h3><p>In a recent comparative effectiveness trial, patients with chronic pain receiving cognitive behavioral therapy supported by artificial intelligence and digital feedback (AI-CBT-CP) were more likely to report clinically meaningful improvements in pain-related disability and intensity at six months than patients randomized to standard telephone CBT-CP. Concerns persist about the impact of AI and digital interventions among socially disadvantaged patients. We examined variation in the proportion of patients completing all treatment sessions and reporting clinically meaningful improvements in pain-related disability and intensity across subgroups of patients defined by social determinants of health (SDOH).</p></div><div><h3>Methods</h3><p>SDOH indicators included age, race, gender, education, income, marital status, geographic access, and clinical severity. Multivariate models with interaction terms tested SDOH indicators as potential moderators of treatment engagement and response to AI-CBT-CP versus standard telephone CBT-CP.</p></div><div><h3>Findings</h3><p>Roughly half of participants (52.9 %) were 65+ years of age, 10.8 % were women, and 19.1 % reported Black race or multiple racial identities. Relatively favorable session completion was observed among patients randomized to AI-CBT-CP across SDOH subgroup, with no groups more likely to complete all session weeks when receiving standard telephone CBT-CP. The relative benefits of AI-CBT-CP in terms of pain-related disability and intensity were generally confirmed across SDOH subgroups. AI-CBT-CP had a greater relative impact on pain-related disability among patients &lt;65 years old (p = .002). In none of the SDOH subgroups, did standard telephone CBT-CP have a greater impact on pain-related disability or intensity than AI-CBT-CP.</p></div><div><h3>Interpretation</h3><p>These findings do not suggest that patients with SDOH disadvantages experience poorer treatment engagement or outcomes when offered CBT-CP supported by AI and digital feedback instead of standard telephone CBT-CP. AI-CBT-CP can help overcome treatment access barriers without exacerbating disparities, benefiting underserved populations with chronic pain.</p></div><div><h3>Funding</h3><p>US Department of Veterans Affairs Health Services Research and Development program.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100164"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000310/pdfft?md5=5c1471fbfd9d71069624e91047fada5d&pid=1-s2.0-S2666521224000310-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040143","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":"Machine-learning-enabled prognostic models for sepsis","authors":"Chunyan Li ,&nbsp;Lu Wang ,&nbsp;Kexun Li ,&nbsp;Hongfei Deng ,&nbsp;Yu Wang ,&nbsp;Li Chang ,&nbsp;Ping Zhou ,&nbsp;Jun Zeng ,&nbsp;Mingwei Sun ,&nbsp;Hua Jiang ,&nbsp;Qi Wang","doi":"10.1016/j.ibmed.2024.100167","DOIUrl":"10.1016/j.ibmed.2024.100167","url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>Sepsis is a leading cause of mortality in intensive care units (ICUs). The development of a robust prognostic model utilizing patients’ clinical data could significantly enhance clinicians’ ability to make informed treatment decisions, potentially improving outcomes for septic patients. This study aims to create a novel machine-learning framework for constructing prognostic tools capable of predicting patient survival or mortality outcome.</div></div><div><h3>Methods:</h3><div>A novel dataset is created using concatenated triples of static data, temporal data, and clinical outcomes to expand data size. This structured input trains five machine learning classifiers (KNN, Logistic Regression, SVM, RF, and XGBoost) with advanced feature engineering. Models are evaluated on an independent cohort using AUROC and a new metric, <span><math><mi>γ</mi></math></span>, which incorporates the F1 score, to assess discriminative power and generalizability.</div></div><div><h3>Results:</h3><div>We developed five prognostic models using the concatenated triple dataset with 10 dynamic features from patient medical records. Our analysis shows that the Extreme Gradient Boosting (XGBoost) model (AUROC = 0.777, F1 score = 0.694) and the Random Forest (RF) model (AUROC = 0.769, F1 score = 0.647), when paired with an ensemble under-sampling strategy, outperform other models. The RF model improves AUROC by 6.66% and reduces overfitting by 54.96%, while the XGBoost model shows a 0.52% increase in AUROC and a 77.72% reduction in overfitting. These results highlight our framework’s ability to enhance predictive accuracy and generalizability, particularly in sepsis prognosis.</div></div><div><h3>Conclusion:</h3><div>This study presents a novel modeling framework for predicting treatment outcomes in septic patients, designed for small, imbalanced, and high-dimensional datasets. By using temporal feature encoding, advanced sampling, and dimension reduction techniques, our approach enhances standard classifier performance. The resulting models show improved accuracy with limited data, offering valuable prognostic tools for sepsis management. This framework demonstrates the potential of machine learning in small medical datasets.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100167"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578721","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":"Novel efficient feature selection: Classification of medical and immunotherapy treatments utilising Random Forest and Decision Trees","authors":"Ahsanullah Yunas Mahmoud","doi":"10.1016/j.ibmed.2024.100151","DOIUrl":"10.1016/j.ibmed.2024.100151","url":null,"abstract":"<div><p>Immunotherapy is an important topic in healthcare as it affects patients' treatments for breast cancer, diabetes, and immunotherapy. However, immunotherapy for warts is less representative because of the lack of data. Machine learning is frequently utilised for treatment diagnosis by converting raw immunotherapy data into useful insights. Efficient classification of immunotherapy treatments is crucial for a productive diagnosis. This study considers immunotherapy with a data-driven and ’less is more perspective’. Despite using a portion of the available imbalance and complex data, the process of diagnosis of immunotherapy treatment is made reasonably precise by considering the parameters of accuracy, sensitivity, and specificity. The contribution of this study is focused on ”more is less” feature selection, which states that approximately 80 % of the effects or results of a system are caused by 20 % of the inputs. The features that contribute most to the classification of immunotherapy treatments are prioritised. This study proposes the implementation of Random Forest and Decision Trees for the classification of immunotherapy treatments. The relevant experimental medical data are explored as a case study. The experiments are conducted using Weka and Python data analysis tools, performing data preprocessing, class balancing, and feature selection. Random Forest performed better than the Decision Trees. By Applying Random Forest and utilising only one feature (time) as an input variable, a classification accuracy of 88.88 %, sensitivity of 95.45 %, and specificity of 60 % are attained. By using 12.5 % of the dataset, when implementing Random Forest together with ordinary feature selection, the diagnosis of immunotherapy treatments is become more efficient, despite using a portion of data features reasonable results are obtained.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000188/pdfft?md5=e93dc97987b02f29f0f70f8ab813e2a6&pid=1-s2.0-S2666521224000188-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708632","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":"Temporal convolutional network on Raman shift for human osteoblast cells fingerprint Analysisa,b,c","authors":"Dario Morganti ,&nbsp;Maria Giovanna Rizzo ,&nbsp;Massimo Orazio Spata ,&nbsp;Salvatore Guglielmino ,&nbsp;Barbara Fazio ,&nbsp;Sebastiano Battiato ,&nbsp;Sabrina Conoci","doi":"10.1016/j.ibmed.2024.100183","DOIUrl":"10.1016/j.ibmed.2024.100183","url":null,"abstract":"<div><div>The physiological state and biological characteristics of cells play a crucial role in the study of several biological mechanisms that are at the basis of the life. Raman spectroscopy, a powerful non-destructive technique, has shown promise in providing unique molecular fingerprints of cells based on their vibrational states. However, the high-dimensional and noisy nature of Raman spectra poses significant challenges in precise cell classification. In this study, we present a novel deep learning algorithm tailored for human cells fingerprint assignment through Raman shift analysis. The proposed deep learning framework harnesses the power of Temporal Convolutional Networks (TCN) to efficiently extract and process Raman spectra information. Leveraging a dataset of labeled Raman spectra, the model is trained to learn discriminative features that capture the subtle differences in cell composition and molecular structures in differential states. Additionally, the proposed model enables real-time cell fingerprint prediction, making it highly applicable for high-throughput analysis in large-scale experiments. Experimental results demonstrate a peak accuracy of 99 %, showcasing the effectiveness and precision of the approach. Overall, the developed deep learning algorithm offers a robust and efficient solution for cell fingerprint assignment through Raman shift analysis, opening new avenues for advancements in physiological and biochemical studies.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532611","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":"A novel automated system to detect breast cancer from ultrasound images using deep fused features with super resolution","authors":"Md Nur-A Alam ,&nbsp;Khandaker Mohammad Mohi Uddin ,&nbsp;Md Mahbubur Rahman ,&nbsp;M.M.R. Manu ,&nbsp;Mostofa Kamal Nasir","doi":"10.1016/j.ibmed.2024.100149","DOIUrl":"https://doi.org/10.1016/j.ibmed.2024.100149","url":null,"abstract":"<div><p>Cancer patients can benefit from early detection and diagnosis. This study proposes a machine vision strategy for detecting breast cancer in ultrasound images and correcting several ultrasound difficulties such artifacts, speckle noise, and blurring. In quantitative evolution, edge preservation criteria were discovered to be superior to standard ones for hybrid anisotropic diffusion. A learnable super-resolution (SR) is inserted in the deep CNN to dig for extra possible information. The feature is fused with a pre-trained deep CNN model utilizing Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP). Machine learning (ML) techniques that are used to create these recommendation systems require well-balanced data in terms of class distribution, however most datasets in the real world are imbalanced. Imbalanced data forces a classifier to concentrate on the majority class while ignoring other classes of interest, lowering the predicted performance of any classification model. We propose a generative adversarial networks (GAN) strategy to overcome the data imbalance problem and improve the performance of recommendation systems in this research. Standard data is used to train this model, which assures a high level of resolution. In the testing phase, generalized data of varied resolution is used to evaluate the model's performance. It is discovered through cross-validation that a 5-fold method can successfully eliminate the overfitting problem. With an accuracy of 99.48 %, this suggested feature fusion technique indicates satisfactory performance when compared to current related works. Finally finding cancer region, researcher used U-Net architecture and extract cancer region from BC ultrasound images.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000164/pdfft?md5=b686b67a89246f188c3f0ac0748f2cab&pid=1-s2.0-S2666521224000164-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484126","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":"Mining trauma care flows of patient cohorts","authors":"Mansoureh Yari Eili ,&nbsp;Jalal Rezaeenour ,&nbsp;Amir Jalaly Bidgoly","doi":"10.1016/j.ibmed.2024.100150","DOIUrl":"https://doi.org/10.1016/j.ibmed.2024.100150","url":null,"abstract":"<div><h3>Background</h3><p>Accurate assessment of trauma in the least time and efficient and effective treatment is gaining momentum in traumatology. Mapping the real-world practice patterns is essential in identifying and improving the quality of care for emergent time-dependent medical states like trauma.</p></div><div><h3>Methods</h3><p>The data mining solutions are extended to the National Trauma Registry of Iran (NTRI) event data by incorporating process mining techniques to ease the analysis, of the associations between clinical pathways and patient cohorts in understanding their performance. A total of 4498 cases, 44,344 events, and 104 different activities within the years 2017–2021 constitute the statistical data. Based on clinically relevant attributes and derived process characteristics the K-means clustering is applied to cohorts followed by comparing the clustering results and treatment pathways.</p></div><div><h3>Results</h3><p>The attributes influence treatment patterns in trauma care flows with the possibility of explaining the variations within cohorts' results. Although these attributes are not involved in the clustering algorithm, there exist meaningful correlations among the cohorts’ members in terms of type (final diagnostics) of injury, Injury Severity Score (minor: 1 &lt; ISS&lt;8; moderate: 9 &lt; ISS&lt;15; sever: 16 &lt; ISS&lt;24), Hospital Length of Stay (HLOS), and treatment activities.</p></div><div><h3>Conclusion</h3><p>Our findings provide more details on the existing process mining techniques and allow easy assessment of the quality of care at a given institution. This approach is an essential data analysis stage to improve complex care processes by proportioning the patient records into closely related groups applicable in target process-aware recommendation initiatives.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100150"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000176/pdfft?md5=32f50ae4c744c31b5ab23aa8fe240631&pid=1-s2.0-S2666521224000176-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541119","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":"Investigating deep-learning NLP for automating the extraction of oncology efficacy endpoints from scientific literature","authors":"Aline Gendrin-Brokmann ,&nbsp;Eden Harrison ,&nbsp;Julianne Noveras ,&nbsp;Leonidas Souliotis ,&nbsp;Harris Vince ,&nbsp;Ines Smit ,&nbsp;Francisco Costa ,&nbsp;David Milward ,&nbsp;Sashka Dimitrievska ,&nbsp;Paul Metcalfe ,&nbsp;Emilie Louvet","doi":"10.1016/j.ibmed.2024.100152","DOIUrl":"https://doi.org/10.1016/j.ibmed.2024.100152","url":null,"abstract":"<div><h3>Objective</h3><p>Benchmarking drug efficacy is a critical step in clinical trial design and planning. The challenge is that much of the data on efficacy endpoints is stored in scientific papers in free text form, so extraction of such data is currently a largely manual task. Our objective is to automate this task as much as possible.</p></div><div><h3>Methods</h3><p>In this study we have developed and optimised a framework to extract efficacy endpoints from text in scientific papers, using a machine learning approach.</p></div><div><h3>Results</h3><p>Our machine learning model predicts 25 classes associated with efficacy endpoints and leads to high F1 scores (harmonic mean of precision and recall) of 96.4 % on the test set, and 93.9 % and 93.7 % on two case studies.</p></div><div><h3>Conclusion</h3><p>These methods were evaluated against – and showed strong agreement with – subject matter experts and show significant promise in the future of automating the extraction of clinical endpoints from free text.</p></div><div><h3>Significance</h3><p>Clinical information extraction from text data is currently a laborious manual task which scales poorly and is prone to human error. Demonstrating the ability to extract efficacy endpoints automatically shows great promise for accelerating clinical trial design moving forwards.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266652122400019X/pdfft?md5=a92e134878dd46a959c3a33708e38779&pid=1-s2.0-S266652122400019X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582934","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":"Context based ranking strategies for renowned instructional methodologies","authors":"Saranya V ,&nbsp;Azween Abdullah ,&nbsp;Parthasarathy Ramadass ,&nbsp;Saravanan Srinivasan ,&nbsp;Basu Dev Shivahare ,&nbsp;Sandeep Kumar Mathivanan ,&nbsp;Karthik P","doi":"10.1016/j.ibmed.2024.100186","DOIUrl":"10.1016/j.ibmed.2024.100186","url":null,"abstract":"<div><div>The main objective of this work is to validate the decisions made towards adoption of appropriate instructional methodologies based on the context of a specific region considering the quality of education, the cost of education and the learning outcomes as predominant parameters. The non-deterministic events and uncertain situations that may arise over a long-range period impose a vague and fuzzy environment in the educational system. Investigations have been made to identify suitable educational framework for implementation in the institutions of a specific region in view of these unpredictable events and non-deterministic conditions. Fuzzy decision analysis and rough set theory have been applied to rank the prominent instructional methodologies which are encompassed within each educational framework. Hurwicz Rule is adopted to balance the pessimistic and optimistic opinions about the non-deterministic events while validating the merits of the instructional methodologies. Grey relational analysis is carried out while ranking instructional methodologies in a vague environment. In this work, the instructional methodologies are ranked using fuzzy entropy as well as crisp entropy measures and the outcomes of the fuzzy and rough sets-based decision analysis have been validated.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662591","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":"An effective U-net model for diagnosing Covid-19 infection","authors":"Shirin Kordnoori ,&nbsp;Maliheh Sabeti ,&nbsp;Hamidreza Mostafaei ,&nbsp;Saeed Seyed Agha Banihashemi","doi":"10.1016/j.ibmed.2024.100156","DOIUrl":"10.1016/j.ibmed.2024.100156","url":null,"abstract":"<div><p>Coronavirus disease 2019 (COVID-19) has become a pandemic all over the world and has spread rapidly. To distinguish between infected and non-infected areas, there is a critical need for segmentation methods that can identify infected areas from Chest Computed Tomography (CT) scans. In recent years, deep learning has become the most widely used approach for medical image segmentation, including the identification of infected areas in Chest CT scans. We propose an encoder-decoder based on the U-NET architecture for segmenting the MedSeg dataset, which contains lung CT scans. To study the effect of input dimensions on the model's output results, we gave CT images with dimensions of 224 × 224, 256 × 256, and 512 × 512 as inputs to the model. The results showed that 224 × 224 achieved higher results compared to 256 × 256 and 512 × 512, with a <span><math><mrow><mi>d</mi><mi>i</mi><mi>c</mi><msub><mi>e</mi><mrow><mi>c</mi><mi>o</mi><mi>e</mi><mi>f</mi></mrow></msub></mrow></math></span> of 81.36, accuracy of 87.65, sensitivity of 84.71, and specificity of 88.35. Additionally, the 224 × 224 input based on the proposed model achieved the highest number of correct answers compared to previous U-net methods. The proposed model can be applied as an effective screening tool to help primary service staff better refer suspected patients to specialists.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000231/pdfft?md5=b94ffe85fb10dc2d0a608b570ad4353c&pid=1-s2.0-S2666521224000231-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629823","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":"Improving the quality of pulmonary nodules segmentation using the new proposed U-Net neural network","authors":"A. Sadremomtaz,&nbsp;M. Zadnorouzi","doi":"10.1016/j.ibmed.2024.100166","DOIUrl":"10.1016/j.ibmed.2024.100166","url":null,"abstract":"<div><p>Diagnosing lung cancer is difficult due to the complexity of the nature of nodules. CT scan imaging is the most common imaging to diagnosis of lung cancer. Detection of nodules from these images is a challenge for radiologists and doctors. In recent years, neural networks have been developed for automatic, faster and more accurate diagnosis of diseases from medical images. In the present study, a new improved U-Net neural network is introduced for the automatic detection and segmentation of pulmonary nodules. The evaluation of this model has been done on LIDC-IDRI database. Our results have high values of recall, specificity and accuracy. The highest Recall value is 97.97 and is related to Juxtra-vascular. Specificity and accuracy for non-solid, partially solid and tiny has a value of 96.99.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100166"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000334/pdfft?md5=e97dbb0434a7786853114c1216a89ad7&pid=1-s2.0-S2666521224000334-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049535","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}