Intelligence-based medicine最新文献_第10页

Temporal convolutional network on Raman shift for human osteoblast cells fingerprint Analysisa,b,c 用于人类成骨细胞指纹分析的拉曼移动时序卷积网络a,b,c

Intelligence-based medicine Pub Date : 2024-01-01 DOI: 10.1016/j.ibmed.2024.100183

Dario Morganti , Maria Giovanna Rizzo , Massimo Orazio Spata , Salvatore Guglielmino , Barbara Fazio , Sebastiano Battiato , Sabrina Conoci

{"title":"Temporal convolutional network on Raman shift for human osteoblast cells fingerprint Analysisa,b,c","authors":"Dario Morganti , Maria Giovanna Rizzo , Massimo Orazio Spata , Salvatore Guglielmino , Barbara Fazio , Sebastiano Battiato , 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}

引用次数: 0

A novel automated system to detect breast cancer from ultrasound images using deep fused features with super resolution 利用超分辨率深度融合特征从超声波图像中检测乳腺癌的新型自动系统

Intelligence-based medicine Pub Date : 2024-01-01 DOI: 10.1016/j.ibmed.2024.100149

Md Nur-A Alam , Khandaker Mohammad Mohi Uddin , Md Mahbubur Rahman , M.M.R. Manu , Mostofa Kamal Nasir

{"title":"A novel automated system to detect breast cancer from ultrasound images using deep fused features with super resolution","authors":"Md Nur-A Alam , Khandaker Mohammad Mohi Uddin , Md Mahbubur Rahman , M.M.R. Manu , 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}

引用次数: 0

Mining trauma care flows of patient cohorts 挖掘患者群体的创伤护理流程

Intelligence-based medicine Pub Date : 2024-01-01 DOI: 10.1016/j.ibmed.2024.100150

Mansoureh Yari Eili , Jalal Rezaeenour , Amir Jalaly Bidgoly

{"title":"Mining trauma care flows of patient cohorts","authors":"Mansoureh Yari Eili , Jalal Rezaeenour , 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 < ISS<8; moderate: 9 < ISS<15; sever: 16 < ISS<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}

引用次数: 0

Investigating deep-learning NLP for automating the extraction of oncology efficacy endpoints from scientific literature 从科学文献中自动提取肿瘤疗效终点的深度学习 NLP 研究

Intelligence-based medicine Pub Date : 2024-01-01 DOI: 10.1016/j.ibmed.2024.100152

Aline Gendrin-Brokmann , Eden Harrison , Julianne Noveras , Leonidas Souliotis , Harris Vince , Ines Smit , Francisco Costa , David Milward , Sashka Dimitrievska , Paul Metcalfe , Emilie Louvet

{"title":"Investigating deep-learning NLP for automating the extraction of oncology efficacy endpoints from scientific literature","authors":"Aline Gendrin-Brokmann , Eden Harrison , Julianne Noveras , Leonidas Souliotis , Harris Vince , Ines Smit , Francisco Costa , David Milward , Sashka Dimitrievska , Paul Metcalfe , 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}

引用次数: 0

Context based ranking strategies for renowned instructional methodologies 基于情境的知名教学方法排名策略

Intelligence-based medicine Pub Date : 2024-01-01 DOI: 10.1016/j.ibmed.2024.100186

Saranya V , Azween Abdullah , Parthasarathy Ramadass , Saravanan Srinivasan , Basu Dev Shivahare , Sandeep Kumar Mathivanan , Karthik P

{"title":"Context based ranking strategies for renowned instructional methodologies","authors":"Saranya V , Azween Abdullah , Parthasarathy Ramadass , Saravanan Srinivasan , Basu Dev Shivahare , Sandeep Kumar Mathivanan , 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}

引用次数: 0

An effective U-net model for diagnosing Covid-19 infection 诊断 Covid-19 感染的有效 U 网模型

Intelligence-based medicine Pub Date : 2024-01-01 DOI: 10.1016/j.ibmed.2024.100156

Shirin Kordnoori , Maliheh Sabeti , Hamidreza Mostafaei , Saeed Seyed Agha Banihashemi

{"title":"An effective U-net model for diagnosing Covid-19 infection","authors":"Shirin Kordnoori , Maliheh Sabeti , Hamidreza Mostafaei , 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}

引用次数: 0

Improving the quality of pulmonary nodules segmentation using the new proposed U-Net neural network 利用新提出的 U-Net 神经网络提高肺结节分割质量

Intelligence-based medicine Pub Date : 2024-01-01 DOI: 10.1016/j.ibmed.2024.100166

A. Sadremomtaz, M. Zadnorouzi

引用次数: 0

A Convolutional Neural Network- Based Deep Learning To Detect Reticulocytes From Human Peripheral Blood 基于卷积神经网络的深度学习检测人体外周血中的网状细胞

Intelligence-based medicine Pub Date : 2024-01-01 DOI: 10.1016/j.ibmed.2024.100175

Keerthy Reghunandanan , V.S. Lakshmi , Rose Raj , Kasi Viswanath , Christeen Davis , Rajesh Chandramohanadas

{"title":"A Convolutional Neural Network- Based Deep Learning To Detect Reticulocytes From Human Peripheral Blood","authors":"Keerthy Reghunandanan , V.S. Lakshmi , Rose Raj , Kasi Viswanath , Christeen Davis , Rajesh Chandramohanadas","doi":"10.1016/j.ibmed.2024.100175","DOIUrl":"10.1016/j.ibmed.2024.100175","url":null,"abstract":"<div><div>Machine learning approaches are rapidly augmenting, and in some cases, replacing the conventional methods in biomedical data analysis; to reduce time, cost, biases, and the need for sophisticated analytical platforms. Hence, significant interest has been compounded in the integration of automated image analysis for various clinical applications, such as the detection of infected or inflamed wounds, bone fractures or for the purpose of disease diagnosis – such as <em>Plasmodium</em> parasites or circulating tumour cells in blood. Here, we report the development of a Convolutional Neural Network (CNN)-based method on CPU to distinguish and count immature human red blood cells known as reticulocytes from blood smears. Reticulocytes represent a heterogeneous and relatively small percentage of cells in peripheral blood, and contain residual RNA in complex with proteins which generates thread-like patterns when stained with New Methylene Blue (NMB) dye. We used more than 200 NMB-stained images from leukocyte-depleted blood to train and optimize the model for immature reticulocytes (stained positive with NMB, intensity and pattern of which depends on the developmental stage of the reticulocyte) and mature RBCs (no staining with NMB). The training performance evaluation metrics demonstrated a mean average precision (mAP50) of 0.88, a precision of 0.83, a recall of 0.88, and an F1 score of 0.87. Our model was able to successfully count reticulocytes with accuracy more than 90% from unknown samples which were subsequently cross-verified through microscopy and counting. Given the importance of reticulocyte maturation and its clinical relevance, the newly developed model will find important, easy to adopt biomedical applications that can be achieved on a simple PC.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100175"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578622","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}

引用次数: 0

Prognostics for respiratory epidemic dynamics by multivariate gaidai risk assessment methodology 通过多变量外代风险评估方法进行呼吸道流行病动态预报

Intelligence-based medicine Pub Date : 2024-01-01 DOI: 10.1016/j.ibmed.2024.100173

Oleg Gaidai , Hongchen Li , Yu Cao , Alia Ashraf , Yan Zhu

{"title":"Prognostics for respiratory epidemic dynamics by multivariate gaidai risk assessment methodology","authors":"Oleg Gaidai , Hongchen Li , Yu Cao , Alia Ashraf , Yan Zhu","doi":"10.1016/j.ibmed.2024.100173","DOIUrl":"10.1016/j.ibmed.2024.100173","url":null,"abstract":"<div><h3>Introduction</h3><div>current study introduces an accurate prediction spatiotemporal model for epidemic outbreaks risk assessment.</div></div><div><h3>Methods</h3><div>utilize state-of-the-art statistical methodology on raw/unfiltered clinical datasets. In order to provide trustworthy long-term forecasts of viral outbreak risks, this research suggests a novel biosystem bio-reliability approach that works particularly well for multi-regional biological, environmental, and public health systems that are monitored over a representative time-lapse.</div></div><div><h3>Results</h3><div>study made use of daily clinically reported patient counts from COVID-19 (SARS-CoV-2) throughout all impacted Dutch administrative areas. The objective of this research was to establish new benchmark for novel bio-reliability methodology that enables efficient risk analysis, based on recorded raw clinical patient numbers, with accounting for pertinent area mapping.</div></div><div><h3>Remarks and significance</h3><div>by effectively employing various clinical survey datasets that are now accessible, the proposed technique may be used for contemporary biomedical applications, as well as the general welfare.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100173"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442514","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}

引用次数: 0

Exploring the business aspects of digital pathology, deep learning in cancers 探索数字病理学的业务方面，癌症中的深度学习

Intelligence-based medicine Pub Date : 2024-01-01 DOI: 10.1016/j.ibmed.2024.100172

Arjun Reddy , Darnell K. Adrian Williams , Gillian Graifman , Nowair Hussain , Maytal Amiel , Tran Priscilla , Ali Haider , Bali Kumar Kavitesh , Austin Li , Leael Alishahian , Nichelle Perera , Corey Efros , Myoungmee Babu , Mathew Tharakan , Mill Etienne , Benson A. Babu

{"title":"Exploring the business aspects of digital pathology, deep learning in cancers","authors":"Arjun Reddy , Darnell K. Adrian Williams , Gillian Graifman , Nowair Hussain , Maytal Amiel , Tran Priscilla , Ali Haider , Bali Kumar Kavitesh , Austin Li , Leael Alishahian , Nichelle Perera , Corey Efros , Myoungmee Babu , Mathew Tharakan , Mill Etienne , Benson A. Babu","doi":"10.1016/j.ibmed.2024.100172","DOIUrl":"10.1016/j.ibmed.2024.100172","url":null,"abstract":"<div><h3>Introduction</h3><div>Cancer remains one of the leading causes of morbidity and mortality worldwide. Deep learning in digital pathology has the potential to improve operational efficiency, costs, and care.</div></div><div><h3>Methods</h3><div>We searched Web of Science, Arxiv, MedRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, and Cochrane databases for whole slide imaging and deep learning articles published between 2019 and 2023. The final six articles were selected from 776 articles identified through an inclusion criterion.</div></div><div><h3>Conclusion</h3><div>Digital pathology services that utilize deep learning have the potential to enhance clinical workflow efficiencies and can have a positive impact on business activities. We anticipate cost reductions as deep learning technology advances and more companies enter the digital pathology ecosystem. However, the limited availability of business use cases, primarily due to publication bias, poses a challenge in medicine without clear examples to learn from.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100172"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586836","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}

引用次数: 0