Journal of Pathology Informatics最新文献

{"title":"AI-algorithm training and validation for identification of endometrial CD138+ cells in infertility-associated conditions; polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF)","authors":"Seungbaek Lee ,&nbsp;Riikka K. Arffman ,&nbsp;Elina K. Komsi ,&nbsp;Outi Lindgren ,&nbsp;Janette A. Kemppainen ,&nbsp;Hanna Metsola ,&nbsp;Henna-Riikka Rossi ,&nbsp;Anne Ahtikoski ,&nbsp;Keiu Kask ,&nbsp;Merli Saare ,&nbsp;Andres Salumets ,&nbsp;Terhi T. Piltonen","doi":"10.1016/j.jpi.2024.100380","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100380","url":null,"abstract":"<div><h3>Background</h3><p>Endometrial CD138+ plasma cells serve as a diagnostic biomarker for endometrial inflammation, and their elevated occurrence correlates positively with adverse pregnancy outcomes. Infertility-related conditions like polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) are closely associated with systemic and local chronic inflammatory status, wherein endometrial CD138+ plasma cell accumulation could also contribute to endometrial pathology. Current methods for quantifying CD138+ cells typically involve laborious and time-consuming microscopic assessments of only a few random areas from a slide. These methods have limitations in accurately representing the entire slide and are susceptible to significant biases arising from intra- and interobserver variations. Implementing artificial intelligence (AI) for CD138+ cell identification could enhance the accuracy, reproducibility, and reliability of analysis.</p></div><div><h3>Methods</h3><p>Here, an AI algorithm was developed to identify CD138+ plasma cells within endometrial tissue. The AI model comprised two layers of convolutional neural networks (CNNs). CNN1 was trained to segment epithelium and stroma across 28,363 mm² (2.56 mm² of epithelium and 24.87 mm² of stroma), while CNN2 was trained to distinguish stromal cells based on CD138 staining, encompassing 7345 cells in the object layers (6942 CD138− cells and 403 CD138+ cells). The training and performance of the AI model were validated by three experienced pathologists. We collected 193 endometrial tissues from healthy controls (<em>n</em> = 73), women with PCOS (<em>n</em> = 91), and RIF patients (<em>n</em> = 29) and compared the CD138+ cell percentages based on cycle phases, ovulation status, and endometrial receptivity utilizing the AI model.</p></div><div><h3>Results</h3><p>The AI algorithm consistently and reliably distinguished CD138− and CD138+ cells, with total error rates of 6.32% and 3.23%, respectively. During the training validation, there was a complete agreement between the decisions made by the pathologists and the AI algorithm, while the performance validation demonstrated excellent accuracy between the AI and human evaluation methods (intraclass correlation; 0.76, 95% confidence intervals; 0.36–0.93, <em>p</em> = 0.002) and a positive correlation (Spearman's rank correlation coefficient: 0.79, <em>p</em> &lt; 0.01). In the AI analysis, the AI model revealed higher CD138+ cell percentages in the proliferative phase (PE) endometrium compared to the secretory phase or anovulatory PCOS endometrium, irrespective of PCOS diagnosis. Interestingly, CD138+ percentages differed according to PCOS phenotype in the PE (<em>p</em> = 0.03). On the other hand, the receptivity status had no impact on the cell percentages in RIF samples.</p></div><div><h3>Conclusion</h3><p>Our findings emphasize the potential and accuracy of the AI algorithm in detecting endometrial ","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000191/pdfft?md5=e87bcb34b2122d5de74031fb378126b2&pid=1-s2.0-S2153353924000191-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084391","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":"Understanding the financial aspects of digital pathology: A dynamic customizable return on investment calculator for informed decision-making","authors":"Orly Ardon ,&nbsp;Sylvia L. Asa ,&nbsp;Mark C. Lloyd ,&nbsp;Giovanni Lujan ,&nbsp;Anil Parwani ,&nbsp;Juan C. Santa-Rosario ,&nbsp;Bryan Van Meter ,&nbsp;Jennifer Samboy ,&nbsp;Danielle Pirain ,&nbsp;Scott Blakely ,&nbsp;Matthew G. Hanna","doi":"10.1016/j.jpi.2024.100376","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100376","url":null,"abstract":"<div><h3>Background</h3><p>The adoption of digital pathology has transformed the field of pathology, however, the economic impact and cost analysis of implementing digital pathology solutions remain a critical consideration for institutions to justify. Digital pathology implementation requires a thorough evaluation of associated costs and should identify and optimize resource allocation to facilitate informed decision-making. A dynamic cost calculator to estimate the financial implications of deploying digital pathology systems was needed to estimate the financial effects on transitioning to a digital workflow.</p></div><div><h3>Methods</h3><p>A systematic approach was used to comprehensively assess the various components involved in implementing and maintaining a digital pathology system. This consisted of: (1) identification of key cost categories associated with digital pathology implementation; (2) data collection and analysis of cost estimation; (3) cost categorization and quantification of direct and indirect costs associated with different use cases, allowing customization of each factor based on specific intended uses and market rates, industry standards, and regional variations; (4) opportunities for savings realized by digitization of glass slides and (5) integration of the cost calculator into a unified framework for a holistic view of the financial implications associated with digital pathology implementation. The online tool enables the user to test various scenarios specific to their institution and provides adjustable parameters to assure organization specific relatability.</p></div><div><h3>Results</h3><p>The Digital Pathology Association has developed a web-based calculator as a companion tool to provide an exhaustive list of the necessary concepts needed when assessing the financial implications of transitioning to a digital pathology system. The dynamic return on investment (ROI) calculator successfully integrated relevant cost and cost-saving components associated with digital pathology implementation and maintenance. Considerations include factors such as digital pathology infrastructure, clinical operations, staffing, hardware and software, information technology, archive and retrieval, medical–legal, and potential reimbursements. The ROI calculator developed for digital pathology workflows offers a comprehensive, customizable tool for institutions to assess their anticipated upfront and ongoing annual costs as they start or expand their digital pathology journey. It also offers cost-savings analysis based on specific user case volume, institutional geographic considerations, and actual costs. In addition, the calculator also serves as a tool to estimate number of required whole slide scanners, scanner throughput, and data storage (TB). This tool is intended to estimate the potential costs and cost savings resulting from the transition to digital pathology for business plan justifications and return on investment calculation","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000154/pdfft?md5=06d1c9d9fe955e29390f2e8c6402cf66&pid=1-s2.0-S2153353924000154-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825437","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":"Evaluation of the precision and accuracy in the classification of breast histopathology images using the MobileNetV3 model","authors":"Kenneth DeVoe ,&nbsp;Gary Takahashi ,&nbsp;Ebrahim Tarshizi ,&nbsp;Allan Sacker","doi":"10.1016/j.jpi.2024.100377","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100377","url":null,"abstract":"<div><p>Accurate surgical pathological assessment of breast biopsies is essential to the proper management of breast lesions. Identifying histological features, such as nuclear pleomorphism, increased mitotic activity, cellular atypia, patterns of architectural disruption, as well as invasion through basement membranes into surrounding stroma and normal structures, including invasion of vascular and lymphatic spaces, help to classify lesions as malignant. This visual assessment is repeated on numerous slides taken at various sections through the resected tumor, each at different magnifications. Computer vision models have been proposed to assist human pathologists in classification tasks such as these. Using MobileNetV3, a convolutional architecture designed to achieve high accuracy with a compact parameter footprint, we attempted to classify breast cancer images in the BreakHis_v1 breast pathology dataset to determine the performance of this model out-of-the-box. Using transfer learning to take advantage of ImageNet embeddings without special feature extraction, we were able to correctly classify histopathology images broadly as benign or malignant with 0.98 precision, 0.97 recall, and an F1 score of 0.98. The ability to classify into histological subcategories was varied, with the greatest success being with classifying ductal carcinoma (accuracy 0.95), and the lowest success being with lobular carcinoma (accuracy 0.59). Multiclass ROC assessment of performance as a multiclass classifier yielded AUC values ≥0.97 in both benign and malignant subsets. In comparison with previous efforts, using older and larger convolutional network architectures with feature extraction pre-processing, our work highlights that modern, resource-efficient architectures can classify histopathological images with accuracy that at least matches that of previous efforts, without the need for labor-intensive feature extraction protocols. Suggestions to further refine the model are discussed.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000166/pdfft?md5=3267cff7f293c28129100035b0de0ddd&pid=1-s2.0-S2153353924000166-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140646007","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":"On image search in histopathology","authors":"H.R. Tizhoosh ,&nbsp;Liron Pantanowitz","doi":"10.1016/j.jpi.2024.100375","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100375","url":null,"abstract":"<div><p>Pathology images of histopathology can be acquired from camera-mounted microscopes or whole-slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons, and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this article, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast, and efficient image search methods in their work.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000142/pdfft?md5=77ba5ac2a9061caa47f0ffdca4b5d013&pid=1-s2.0-S2153353924000142-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140554425","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":"Number of intraepithelial lymphocytes and presence of a subepithelial band in normal colonic mucosa differs according to stainings and evaluation method","authors":"Anne-Marie Kanstrup Fiehn ,&nbsp;Peter Johan Heiberg Engel ,&nbsp;Ulla Engel ,&nbsp;Dea Natalie Munch Jepsen ,&nbsp;Thomas Blixt ,&nbsp;Julie Rasmussen ,&nbsp;Signe Wildt ,&nbsp;Wojciech Cebula ,&nbsp;Andreea-Raluca Diac ,&nbsp;Lars Kristian Munck","doi":"10.1016/j.jpi.2024.100374","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100374","url":null,"abstract":"<div><p>Chronic watery diarrhea is a frequent symptom. In approximately 10% of the patients, a diagnosis of microscopic colitis (MC) is established. The diagnosis relies on specific, but sometimes subtle, histopathological findings. As the histology of normal intestinal mucosa vary, discriminating subtle features of MC from normal tissue can be challenging and therefore auxiliary stainings are increasingly used. The aim of this study was to determine the variance in number of intraepithelial lymphocytes (IELs) and presence of a subepithelial band in normal ileum and colonic mucosa, according to different stains and digital assessment. Sixty-one patients without diarrhea referred to screening colonoscopy due to a positive feacal blood test and presenting with endoscopically normal mucosa were included. Basic histological features, number of IELs, and thickness of a subepithelial band was manually evaluated and a deep learning-based algorithm was developed to digitally determine the number of IELs in each of the two compartments; surface epithelium and cryptal epithelium, and the density of lymphocytes in the lamina propria compartment. The number of IELs was significantly higher on CD3-stained slides compared with slides stained with Hematoxylin-and-Eosin (HE) (<em>p</em>&lt;0.001), and even higher numbers were reached using digital analysis. No significant difference between right and left colon in IELs or density of CD3-positive lymphocytes in lamina propria was found. No subepithelial band was present in HE-stained slides while a thin band was visualized on special stains. Conclusively, in this cohort of prospectively collected ileum and colonic biopsies from asymptomatic patients, the range of IELs and detection of a subepithelial collagenous band varied depending on the stain and method used for assessment. As assessment of biopsies from patients with diarrhea constitute a considerable workload in the pathology departments digital image analysis is highly desired. Knowledge provided by the present study highlight important differences that should be considered before introducing this method in the clinic.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000130/pdfft?md5=e89d0ed8c314678bb6d962000d68d253&pid=1-s2.0-S2153353924000130-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140341660","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":"Microcomputed tomography as a diagnostic tool for detection of lymph node metastasis in non-small cell lung cancer: A decision-support approach for pathological examination “A pilot study for method validation”","authors":"Ayten Kayı Cangır ,&nbsp;Süleyman Gökalp Güneş ,&nbsp;Kaan Orhan ,&nbsp;Hilal Özakıncı ,&nbsp;Yusuf Kahya ,&nbsp;Duru Karasoy ,&nbsp;Serpil Dizbay Sak","doi":"10.1016/j.jpi.2024.100373","DOIUrl":"10.1016/j.jpi.2024.100373","url":null,"abstract":"<div><h3>Background</h3><p>Non-small cell lung cancer (NSCLC) patients without lymph node (LN) metastases (pN0) may exhibit different survival rates, even when their T stage is similar. This divergence could be attributed to the current pathology practice, wherein LNs are examined solely in two-dimensional (2D). Unfortunately, adhering to the protocols of 2D pathological examination does not ensure the exhaustive sampling of all excised LNs, thereby leaving room for undetected metastatic foci in the unexplored depths of tissues. The employment of micro-computed tomography (micro-CT) facilitates a three-dimensional (3D) evaluation of all LNs without compromising sample integrity. In our study, we utilized quantitative micro-CT parameters to appraise the metastatic status of formalin-fixed paraffin-embedded (FFPE) LNs.</p></div><div><h3>Methods</h3><p>Micro-CT scans were conducted on 12 FFPEs obtained from 8 NSCLC patients with histologically confirmed mediastinal LN metastases. Simultaneously, whole-slide images from these FFPEs underwent scanning, and 47 regions of interest (ROIs) (17 metastatic foci, 11 normal lymphoid tissues, 10 adipose tissues, and 9 anthracofibrosis) were marked on scanned images. Quantitative structural variables obtained via micro-CT analysis from tumoral and non-tumoral ROIs, were analyzed.</p></div><div><h3>Result</h3><p>Significant distinctions were observed in linear density, connectivity, connectivity density, and closed porosity between tumoral and non-tumoral ROIs, as indicated by kappa coefficients of 1, 0.90, 1, and 1, respectively. Receiver operating characteristic analysis substantiated the differentiation between tumoral and non-tumoral ROIs based on thickness, linear density, connectivity, connectivity density, and the percentage of closed porosity.</p></div><div><h3>Conclusions</h3><p>Quantitative micro-CT parameters demonstrate the ability to distinguish between tumoral and non-tumoral regions of LNs in FFPEs. The discriminatory characteristics of these quantitative micro-CT parameters imply their potential usefulness in developing an artificial intelligence algorithm specifically designed for the 3D identification of LN metastases while preserving the FFPE tissue.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000129/pdfft?md5=fda55927dc961cda1f292ed5e05fb3e2&pid=1-s2.0-S2153353924000129-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140406150","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":"Quantitative digital pathology enables automated and quantitative assessment of inflammatory activity in patients with autoimmune hepatitis","authors":"Piotr Socha ,&nbsp;Elizabeth Shumbayawonda ,&nbsp;Abhishek Roy ,&nbsp;Caitlin Langford ,&nbsp;Paul Aljabar ,&nbsp;Malgorzata Wozniak ,&nbsp;Sylwia Chełstowska ,&nbsp;Elzbieta Jurkiewicz ,&nbsp;Rajarshi Banerjee ,&nbsp;Ken Fleming ,&nbsp;Maciej Pronicki ,&nbsp;Kamil Janowski ,&nbsp;Wieslawa Grajkowska","doi":"10.1016/j.jpi.2024.100372","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100372","url":null,"abstract":"<div><h3>Background</h3><p>Chronic liver disease diagnoses depend on liver biopsy histopathological assessment. However, due to the limitations associated with biopsy, there is growing interest in the use of quantitative digital pathology to support pathologists. We evaluated the performance of computational algorithms in the assessment of hepatic inflammation in an autoimmune hepatitis in which inflammation is a major component.</p></div><div><h3>Methods</h3><p>Whole-slide digital image analysis was used to quantitatively characterize the area of tissue covered by inflammation [Inflammation Density (ID)] and number of inflammatory foci per unit area [Focal Density (FD)] on tissue obtained from 50 patients with autoimmune hepatitis undergoing routine liver biopsy. Correlations between digital pathology outputs and traditional categorical histology scores, biochemical, and imaging markers were assessed. The ability of ID and FD to stratify between low-moderate (both portal and lobular inflammation ≤1) and moderate-severe disease activity was estimated using the area under the receiver operating characteristic curve (AUC).</p></div><div><h3>Results</h3><p>ID and FD scores increased significantly and linearly with both portal and lobular inflammation grading. Both ID and FD correlated moderately-to-strongly and significantly with histology (portal and lobular inflammation; 0.36≤R≤0.69) and biochemical markers (ALT, AST, GGT, IgG, and gamma globulins; 0.43≤R≤0.57). ID (AUC: 0.85) and FD (AUC: 0.79) had good performance for stratifying between low-moderate and moderate-severe inflammation.</p></div><div><h3>Conclusion</h3><p>Quantitative assessment of liver biopsy using quantitative digital pathology metrics correlates well with traditional pathology scores and key biochemical markers. Whole-slide quantification of disease can support stratification and identification of patients with more advanced inflammatory disease activity.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000117/pdfft?md5=506527e4685e9ff0ebf86f565be2119c&pid=1-s2.0-S2153353924000117-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140138142","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":"Crossing the Andes: Challenges and opportunities for digital pathology in Latin America","authors":"Renata A. Coudry ,&nbsp;Emilio A.C.P. Assis ,&nbsp;Fernando Pereira Frassetto ,&nbsp;Angela Marie Jansen ,&nbsp;Leonard Medeiros da Silva ,&nbsp;Rafael Parra-Medina ,&nbsp;Mauro Saieg","doi":"10.1016/j.jpi.2024.100369","DOIUrl":"10.1016/j.jpi.2024.100369","url":null,"abstract":"<div><p>The most widely accepted and used type of digital pathology (DP) is whole-slide imaging (WSI). The USFDA granted two WSI system approvals for primary diagnosis, the first in 2017. In Latin America, DP has the potential to reshape healthcare by enhancing diagnostic capabilities through artificial intelligence (AI) and standardizing pathology reports. Yet, we must tackle regulatory hurdles, training, resource availability, and unique challenges to the region. Collectively addressing these hurdles can enable the region to harness DP’s advantages—enhancing disease diagnosis, medical research, and healthcare accessibility for its population. Americas Health Foundation assembled a panel of Latin American pathologists who are experts in DP to assess the hurdles to implementing it into pathologists’ workflows in the region and provide recommendations for overcoming them. Some key steps recommended include creating a Latin American Society of Digital Pathology to provide continuing education, developing AI models trained on the Latin American population, establishing national regulatory frameworks for protecting the data, and standardizing formats for DP images to ensure that pathologists can collaborate and validate specimens across the various DP platforms.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000087/pdfft?md5=2e2463cde6a90ee91b313f6ab548c3e4&pid=1-s2.0-S2153353924000087-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140463674","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":"ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application","authors":"Rajib Kumar Halder ,&nbsp;Mohammed Nasir Uddin ,&nbsp;Md. Ashraf Uddin ,&nbsp;Sunil Aryal ,&nbsp;Sajeeb Saha ,&nbsp;Rakib Hossen ,&nbsp;Sabbir Ahmed ,&nbsp;Mohammad Abu Tareq Rony ,&nbsp;Mosammat Farida Akter","doi":"10.1016/j.jpi.2024.100371","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100371","url":null,"abstract":"<div><p>Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML‐CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders.</p><p>Web app link: <span>https://rajib-research-kedney-diseases-prediction.onrender.com/</span><svg><path></path></svg></p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000105/pdfft?md5=ed85d934a771241ed567b9de62993e5f&pid=1-s2.0-S2153353924000105-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140112920","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":"BBDash: An Electron-based tool for analyzing blood product utilization","authors":"Jacob Spector ,&nbsp;Adrienne Kennedy ,&nbsp;Elena Nedelcu","doi":"10.1016/j.jpi.2024.100370","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100370","url":null,"abstract":"<div><p>Blood transfusions can be associated with side effects ranging from occasional febrile reactions to extremely rare fatal reactions. Monitoring blood product orders and ensuring appropriate utilization is therefore an important strategy to ensure patient safety. However, data extracted from laboratory information systems can be difficult to interpret. We created BBDash, an Electron-based tool that reads Sunquest reports to create easy-to-interpret graphs related to blood product utilization.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000099/pdfft?md5=262632943c1f336057625f96685997e0&pid=1-s2.0-S2153353924000099-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140138143","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}