Intelligence-based medicine最新文献

{"title":"FDCT-based watermarking for robust and imperceptible medical image protection","authors":"Beggari Ahmed Saïd ,&nbsp;Wali Ali ,&nbsp;Khaldi Amine ,&nbsp;Kafi Med Redouane ,&nbsp;Aditya Kumar Sahu","doi":"10.1016/j.ibmed.2025.100280","DOIUrl":"10.1016/j.ibmed.2025.100280","url":null,"abstract":"<div><div>Security in medical imaging is a pivotal concern within the healthcare domain, prompting exploration into various watermarking techniques designed to embed imperceptible and secure data within medical images. In this study, we introduce a frequency-based medical image watermarking approach that leverages the Fractional Discrete Cosine Transform (FDCT), Mellin Transform, and Schur decomposition to extract the frequency content of the image. This process is followed by the selection of low-frequency coefficients for further transformation using Schur decomposition. The integration of watermark bits occurs through modulation of the obtained Schur coefficients, ensuring robust and secure watermarking without significantly altering the visual quality of the medical images. The experiments conducted on the ocular database illustrate the capacity, imperceptibility, and robustness of the proposed method. The proposed approach achieves a PSNR of 39.38 dB and SSIM of 0.9998, demonstrating excellent imperceptibility with a capacity of 0.07031 bits per pixel (BPP). The method is robust against various attacks, including JPEG compression, noise addition, and geometric transformations, with NCC values consistently above 0.85 for most common image processing operations. This approach successfully achieves a favorable trade-off between imperceptibility and information embedding capacity, ensuring the authenticity and integrity of medical images during transmission.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100280"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing CNN performance in medical imaging with cross-modality pre-training: A study using MobileNetV3","authors":"Mithunraj ,&nbsp;Preethika ,&nbsp;Shruti Mishra ,&nbsp;Sandeep Kumar Satapathy ,&nbsp;Sachi Nandan Mohanty ,&nbsp;Ali B.M. Ali ,&nbsp;Nadia Batool ,&nbsp;Shirin Shomurotova","doi":"10.1016/j.ibmed.2025.100281","DOIUrl":"10.1016/j.ibmed.2025.100281","url":null,"abstract":"<div><div>In this study, we perform an analysis of convolutional neural networks (CNNs) for transfer learning tasks, leveraging cross-organ and cross-modality transfer learning. Initially, we pre-train the models on an unrelated dataset of mammograms (5734 images), using this as an intermediate step before fine-tuning the models with traditional transfer learning (TL) on the ProstateX dataset, which serves as the target dataset. The CNN architecture evaluated was MobileNetV3. We assessed the model performance using the accuracy as the evaluation metric while also reporting segmentation metrics such as Dice coefficient. Our results demonstrate that MobileNetV3 outperformed VGG16 which is commonly used in previous studies in terms of accuracy, achieving an accuracy of 0.99 compared to VGG16. While segmentation specific metrics indicate room for improvement. This study highlights the advantage of cross-organ and cross-modality transfer learning in improving network performance, with MobileNetV3 showing significant potential in transfer learning tasks after pre-training on mammogram data.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100281"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in child development monitoring: A systematic review on usage, outcomes and acceptance","authors":"Lisa Reinhart, A. C. Bischops, Janna-Lina Kerth, Maurus Hagemeister, Bert Heinrichs, Simon Eickhoff, Juergen Dukart, Kerstin Konrad, Ertan Mayatepek, Thomas Meissner","doi":"10.1016/j.ibmed.2024.100134","DOIUrl":"https://doi.org/10.1016/j.ibmed.2024.100134","url":null,"abstract":"","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"40 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139817559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid-Motion-Track: Markerless tracking of fast human motion with deep learning","authors":"Renjie Li ,&nbsp;Chun-yu Lau ,&nbsp;Rebecca J. St George ,&nbsp;Katherine Lawler ,&nbsp;Saurabh Garg ,&nbsp;Son N. Tran ,&nbsp;Quan Bai ,&nbsp;Jane Alty","doi":"10.1016/j.ibmed.2024.100162","DOIUrl":"10.1016/j.ibmed.2024.100162","url":null,"abstract":"<div><p>Human movement patterns reflect central nervous system function. Small deficits in repetitive fast movements, such as slightly slowed finger-tapping or mildly irregular rhythm of stepping, are often an early sign of a neurological disorder. Accessible tools that precisely measure the individual components of fast movements would thus enhance disease detection, monitoring and research. Deep learning-based computer vision methods applied to digital video-recordings hold promise but current state-of-the-art tools, including DeepLabCut (DLC) and other advanced models, fail to accurately track the fastest range of human movements, primarily due to image blur. To solve this, we developed a new end-to-end, Rapid-Motion-Track (RMT) computer vision tool. This study aimed to evaluate the accuracy of RMT compared to DLC and other advanced computer vision tools. 220 finger-tapping tests were performed at frequencies between 0.5Hz and 6Hz and recorded simultaneously with a standard 30 frames/sec 2D laptop camera and a high-speed 250 frames/sec 3D motion tracking system (ground-truth). Bland-Altman plots and paired Welch's <em>t</em>-test were used to quantify the validity of movement features extracted by computer vision methods with the ground-truth. The movement features extracted by RMT (e.g. frequency, speed, variance) exhibited high concurrent validity across all tapping-frequencies. RMT outperformed other computer vision methods for very fast movements &gt;4Hz. RMT also robustly tracked other fast motions including sit-to-stand, head-turning, foot-tapping, and leg agility. This new tool provides an accurate method to precisely and automatically measure even the fastest and finest human movements. It holds potential of wide reach as digital cameras are ubiquitous in homes, clinics and research centres.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100162"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000292/pdfft?md5=528a7c60c9ba2b2a41fea45266e09369&pid=1-s2.0-S2666521224000292-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979664","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":"Outcome prediction for adult mechanically ventilated patients using machine learning models and comparison with conventional statistical methods: A single-centre retrospective study","authors":"Wei Jun Dan Ong ,&nbsp;Chun Hung How ,&nbsp;Woon Hean Keenan Chong ,&nbsp;Faheem Ahmed Khan ,&nbsp;Kee Yuan Ngiam ,&nbsp;Amit Kansal","doi":"10.1016/j.ibmed.2024.100165","DOIUrl":"10.1016/j.ibmed.2024.100165","url":null,"abstract":"<div><p>In this retrospective single-centre study spanning five years (2016–2021) and involving 2368 adult Intensive Care Unit (ICU) patients requiring over 4 h of mechanical ventilation (MV) in a tertiary care hospital, we investigated the feasibility and accuracy of using machine learning (ML) models in predicting outcomes post-ICU discharge compared to conventional statistical methods (CSM). The study aimed to identify associated risk factors impacting these outcomes. Poor outcomes, defined as ICU readmission, mortality, and prolonged hospital stays, affected 40.2 % of the discharged MV patients. The Extreme Gradient Boost (XGBoost) ML model showed superior performance compared to CSM (Area under the receiver operating characteristic curve: 0.693 vs. 0.667; p-value = 0.03). At 95 % specificity, XGBoost displayed enhanced sensitivity (30.6 % vs. 23.8 %) compared to CSM. Risk factors such as Glasgow Coma Score (GCS) and GCS best motor score at ICU discharge, MV duration, ICU length of stay, and Charlson Comorbidity Index were identified. While both ML and CSM exhibited moderate accuracy, the study suggests ML algorithms have the potential for better predictive capabilities and individual risk factor identification, potentially aiding in the improvement of patient outcomes by identifying high-risk patients requiring closer monitoring. Further validation in larger studies is necessary, but the study underscores the potential for real-time application of ML algorithms developed from the increasing availability of electronic medical records (EMR).</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100165"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000322/pdfft?md5=9b5e16fa3de6867cc99501f81f07c14a&pid=1-s2.0-S2666521224000322-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142011163","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":"Oral cancer segmentation and identification system based on histopathological images using MaskMeanShiftCNN and SV-OnionNet","authors":"R. Dharani ,&nbsp;K. Danesh","doi":"10.1016/j.ibmed.2024.100185","DOIUrl":"10.1016/j.ibmed.2024.100185","url":null,"abstract":"<div><h3>Background</h3><div>Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer and a significant threat to public health because of its high mortality rate. Early detection of OSCC is crucial for successful treatment and improved survival rates, but traditional diagnostic methods, such as biopsy, are time-consuming and require expert analysis. Deep learning algorithms have shown promise in detecting various cancers, including OSCC. However, accurately detecting OSCC on histopathological images remains challenging because of tumor heterogeneity.</div></div><div><h3>Methods</h3><div>This study proposes two new deep learning approaches, MaskMeanShiftCNN and SV-OnionNet, for segmenting and identifying OSCC. MaskMeanShiftCNN uses color, texture, and shape features to segment OSCC regions from input images, while SV-OnionNet is suitable for identifying OSCC at an early stage from histopathological images.</div></div><div><h3>Results</h3><div>The proposed approaches outperformed existing methods for OSCC detection, achieving a classification accuracy of 98.94 %, sensitivity of 98.96 %, specificity of 97.18 %, and error rate of 1.05 %. These results demonstrate the effectiveness of the proposed approaches in accurately detecting OSCC and potentially improving the efficiency of OSCC diagnosis.</div></div><div><h3>Conclusion</h3><div>The proposed deep learning approaches, MaskMeanShiftCNN and SV-OnionNet accurately detected OSCC in input and histopathological images. These approaches can improve the efficiency and accuracy of OSCC diagnosis, ultimately improving patient outcomes.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100185"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554775","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":"Cascaded regression with dual CNN frame work for time effective detection of gliomas cancers","authors":"V.K. Deepak ,&nbsp;R. Sarath","doi":"10.1016/j.ibmed.2024.100168","DOIUrl":"10.1016/j.ibmed.2024.100168","url":null,"abstract":"<div><div>The determination of brain tumor growth primarily relies on the histopathological examination of biopsy samples. Tumor segmentation in the brain presents a significant challenge in medical image analysis due to its complexity. The ultimate goal is to accurately identify and isolate tumor regions. For the segmentation of brain tumors, a variety of deep-learning techniques have been developed, and they have produced promising results. However, achieving accurate segmentation requires the integration of multiple image modalities with varying contrasts. This makes manual segmentation impractical for larger studies, despite its accuracy. Deep learning's exceptional performance has made it an attractive method for quantitative analysis. The field of medical image analysis presents distinctive challenges that must be overcome to achieve optimal results. The ongoing strategy is obtrusive, tedious and inclined to manual mistakes. These weaknesses show that it is so fundamental to play out a completely computerized technique for the multi-characterization of cerebrum cancers in view of deep learning. Thus, this paper presents an efficient time-optimized and deep-learning model based on cascade regression (DLCR) to segment the tumor grade in the following stages: Data Acquisition in which data were obtained from the well-known brain repository BRATS2017, which included 215 HGG (High-Grade Gliomas) and 80 LGG (Low-Grade Gliomas) glioma cases. Fully Convolutional Neural Network (FCNN) preprocessing was used to remove noise and anomalies from the raw data, and Gaussian Mixture Model feature extraction was used to extract features from the preprocessed image and finally the proposed DLCR model for grade identification. Experimental findings indicate that the suggested system surpasses other pre-existing models in various aspects (accuracy: 0.96, sensitivity:0.97, precision:0.88).</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532837","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 multioutput classifier model for breast cancer treatment prediction","authors":"Emad Abd Al Rahman ,&nbsp;Nur Intan Raihana Ruhaiyem ,&nbsp;Majed Bouchahma","doi":"10.1016/j.ibmed.2024.100158","DOIUrl":"10.1016/j.ibmed.2024.100158","url":null,"abstract":"<div><p>A growing number of new cases and fatalities occur each year due to breast cancer, making it the most frequent malignancy globally. Utilizing a multioutput classifier technique with algorithms such as CatBoost, XGBoost, NN, and NN Binary, this work presents a new model for predicting breast cancer treatments: surgery, radiotherapy, and chemotherapy. We tackle the pressing need for accurate medical treatments by developing a model to enhance the predicted accuracy of breast cancer treatment outcomes. The model accomplishes impressive results in predicting surgical outcomes; in particular, Neural Networks (NN and NN Binary) perform exceptionally well in terms of recall and precision, reaching 97 % accuracy and 98 % F1-scores. While the model's accuracy is only about 63 % for radiotherapy, it shows a promising recall of up to 84 %. Accuracy and precision in chemotherapy predictions remain stable at 82 %, with AUC-ROC values of up to 89 %, suggesting excellent discrimination ability. By combining multioutput classifiers with sophisticated algorithms, we hope to make treatment prediction models more tailored to individual breast cancer patient profiles, which might usher in a new era of tailored treatment plans and meet the rising demand for precision medicine in cancer care.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100158"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000255/pdfft?md5=495fcee4686f4acc2b598a0adea6e4ab&pid=1-s2.0-S2666521224000255-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839308","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":"Prediction of skin cancer invasiveness: A comparative study among the regions of Brazil","authors":"Marcus Augusto Padilha Mata,&nbsp;Plinio Sa Leitao-Junior","doi":"10.1016/j.ibmed.2024.100157","DOIUrl":"10.1016/j.ibmed.2024.100157","url":null,"abstract":"<div><h3>Context</h3><p>Skin cancer is the most incident neoplasia in Brazil, and their invasiveness can be impacted by various factors, including geographical aspects. Identifying these factors is important for improving diagnosis and treatment.</p></div><div><h3>Objective</h3><p>The research focused on analyzing the impact of region on the invasiveness of skin cancer in Brazil, through the identification of regional predictive patterns.</p></div><div><h3>Methods</h3><p>An analysis and processing of data from the Hospital Cancer Registries (RHC) of Brazil's National Cancer Institute (INCA) were conducted, followed by the application of machine learning algorithms. The SHapley Additive exPlanations (SHAP) approach was employed to provide explanations for the developed artificial intelligence models.</p></div><div><h3>Results</h3><p>It was revealed that geography plays a significant role in predicting the invasiveness of skin cancer, reinforcing the need to consider regional specificities in future studies.</p></div><div><h3>Conclusions</h3><p>The study identified that regional characteristics of Brazil impacts the prediction of the invasiveness of skin cancer. Despite limitations, such as the issue of data imbalance, the findings are important for developing more effective policies in the fight against skin cancer in the Brazil.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100157"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000243/pdfft?md5=bb190d67c65ebb2d6e3c68d16c1ed3cd&pid=1-s2.0-S2666521224000243-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842490","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":"AIoT-based embedded systems optimization using feature selection for Parkinson's disease diagnosis through speech disorders","authors":"Shawki Saleh ,&nbsp;Zakaria Alouani ,&nbsp;Othmane Daanouni ,&nbsp;Soufiane Hamida ,&nbsp;Bouchaib Cherradi ,&nbsp;Omar Bouattane","doi":"10.1016/j.ibmed.2024.100184","DOIUrl":"10.1016/j.ibmed.2024.100184","url":null,"abstract":"<div><div>This study aims to build a pre-diagnosis tool for predicting Parkinson's disease based on a speech disorder which appears as a symptom in approximately 90 % of people with this disease. Recently, some technologies such as AIoT and IoMT aim to integrate Artificial Intelligence and the Internet of Things or Internet of Medical Things to provide an intelligent remote diagnosis for enhancing medical services. Thus, the classification speed and reliability of the systems in these fields are highly recommended. In this work, we compared five ML algorithms (LR, RF, XGB, SVM, KNN) based on their performance, classification speed and reliability. We employed the sequential forward feature selection in order to select the optimal relevant feature for reducing the dimensionality of the used acoustic dataset to enhance both the performance and computation cost for the proposed system. Furthermore, the stratified cross-validation approach has been used to obtain a fair estimation for the proposed system across each point in the dataset. In this paper, we used a vocal dataset of Parkinson's disease consisting of 195 samples and 22 features. We found that 10 features provide the optimal performance. So, we proposed the K-Nearest Neighbours algorithm as a classifier for our system. It reached 98.46 %, 99.33 % and 98.67 % of the accuracy, sensitivity and precision respectively. Moreover, this work provides a detailed explanation of the employed techniques and the obtained results. The novelty of this work, compared to the existing literature, is to enhance both computation cost and performance for building a real-world system to diagnose Parkinson's disease through speech disorder.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532613","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}