BioMedical Engineering OnLine最新文献

{"title":"Application of visual transformer in renal image analysis.","authors":"Yuwei Yin, Zhixian Tang, Huachun Weng","doi":"10.1186/s12938-024-01209-z","DOIUrl":"10.1186/s12938-024-01209-z","url":null,"abstract":"<p><p>Deep Self-Attention Network (Transformer) is an encoder-decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"27"},"PeriodicalIF":2.9,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10913284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140027321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A concept for human use of real-time and remote monitoring of diabetic subjects using intermittent scanned continuous glucose measurement.","authors":"Jhon E Goez-Mora, Natalia Arbeláez-Córdoba, Norman Balcazar-Morales, Pablo S Rivadeneira","doi":"10.1186/s12938-024-01217-z","DOIUrl":"10.1186/s12938-024-01217-z","url":null,"abstract":"<p><strong>Background: </strong>Flash glucose monitoring systems like the FreeStyle Libre (FSL) sensor have gained popularity for monitoring glucose levels in people with diabetes mellitus. This sensor can be paired with an off-label converted real-time continuous glucose monitor (c-rtCGM) plus an ad hoc computer/smartphone interface for remote real-time monitoring of diabetic subjects, allowing for trend analysis and alarm generation.</p><p><strong>Objectives: </strong>This work evaluates the accuracy and agreement between the FSL sensor and the developed c-rtCGM system. As real-time monitoring is the main feature, the system's connectivity was assessed at 5-min intervals during the trials.</p><p><strong>Methods: </strong>One week of glucose data were collected from 16 type 1 diabetic rats using the FSL sensor and the c-rtCGM. Baseline blood samples were taken the first day before inducing type 1 diabetes with streptozotocin. Once confirmed diabetic rats, FSL and c-rtCGM, were implanted, and to improve data matching between the two monitoring devices, the c-rtCGM was calibrated to the FSL glucometer readings. A factorial design 2 × 3^3 and a second-order regression was used to find the base values of the linear model transformation of the raw data obtained from the sensor. Accuracy, agreement, and connectivity were assessed by median absolute relative difference (Median ARD), range averaging times, Parkes consensus error grid analysis (EGA), and Bland-Altman analysis with a non-parametric approach.</p><p><strong>Results: </strong>Compared to the FSL sensor, the c-rtCGM had an overall Median ARD of 6.58%, with 93.06% of results in zone A when calibration was not carried out. When calibration frequency changed from every 50 h to 1 h, the overall Median ARD improved from 6.68% to 2.41%, respectively. The connectivity evaluation showed that 95% of data was successfully received every 5 min by the computer interface.</p><p><strong>Conclusions and clinical importance: </strong>The results demonstrate the feasibility and reliability of real-time and remote subjects with diabetes monitoring using the developed c-rtCGM system. Performing calibrations relative to the FSL readings increases the accuracy of the data displayed at the interface.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"26"},"PeriodicalIF":3.9,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10903066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139989244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HM_ADET: a hybrid model for automatic detection of eyelid tumors based on photographic images.","authors":"Jiewei Jiang, Haiyang Liu, Lang He, Mengjie Pei, Tongtong Lin, Hailong Yang, Junhua Yang, Jiamin Gong, Xumeng Wei, Mingmin Zhu, Guohai Wu, Zhongwen Li","doi":"10.1186/s12938-024-01221-3","DOIUrl":"10.1186/s12938-024-01221-3","url":null,"abstract":"<p><strong>Background: </strong>The accurate detection of eyelid tumors is essential for effective treatment, but it can be challenging due to small and unevenly distributed lesions surrounded by irrelevant noise. Moreover, early symptoms of eyelid tumors are atypical, and some categories of eyelid tumors exhibit similar color and texture features, making it difficult to distinguish between benign and malignant eyelid tumors, particularly for ophthalmologists with limited clinical experience.</p><p><strong>Methods: </strong>We propose a hybrid model, HM_ADET, for automatic detection of eyelid tumors, including YOLOv7_CNFG to locate eyelid tumors and vision transformer (ViT) to classify benign and malignant eyelid tumors. First, the ConvNeXt module with an inverted bottleneck layer in the backbone of YOLOv7_CNFG is employed to prevent information loss of small eyelid tumors. Then, the flexible rectified linear unit (FReLU) is applied to capture multi-scale features such as texture, edge, and shape, thereby improving the localization accuracy of eyelid tumors. In addition, considering the geometric center and area difference between the predicted box (PB) and the ground truth box (GT), the GIoU_loss was utilized to handle cases of eyelid tumors with varying shapes and irregular boundaries. Finally, the multi-head attention (MHA) module is applied in ViT to extract discriminative features of eyelid tumors for benign and malignant classification.</p><p><strong>Results: </strong>Experimental results demonstrate that the HM_ADET model achieves excellent performance in the detection of eyelid tumors. In specific, YOLOv7_CNFG outperforms YOLOv7, with AP increasing from 0.763 to 0.893 on the internal test set and from 0.647 to 0.765 on the external test set. ViT achieves AUCs of 0.945 (95% CI 0.894-0.981) and 0.915 (95% CI 0.860-0.955) for the classification of benign and malignant tumors on the internal and external test sets, respectively.</p><p><strong>Conclusions: </strong>Our study provides a promising strategy for the automatic diagnosis of eyelid tumors, which could potentially improve patient outcomes and reduce healthcare costs.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"25"},"PeriodicalIF":3.9,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10903075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139989245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulating impaired left ventricular-arterial coupling in aging and disease: a systematic review.","authors":"Corina Cheng Ai Ding, Socrates Dokos, Azam Ahmad Bakir, Nurul Jannah Zamberi, Yih Miin Liew, Bee Ting Chan, Nor Ashikin Md Sari, Alberto Avolio, Einly Lim","doi":"10.1186/s12938-024-01206-2","DOIUrl":"10.1186/s12938-024-01206-2","url":null,"abstract":"<p><p>Aortic stenosis, hypertension, and left ventricular hypertrophy often coexist in the elderly, causing a detrimental mismatch in coupling between the heart and vasculature known as ventricular-vascular (VA) coupling. Impaired left VA coupling, a critical aspect of cardiovascular dysfunction in aging and disease, poses significant challenges for optimal cardiovascular performance. This systematic review aims to assess the impact of simulating and studying this coupling through computational models. By conducting a comprehensive analysis of 34 relevant articles obtained from esteemed databases such as Web of Science, Scopus, and PubMed until July 14, 2022, we explore various modeling techniques and simulation approaches employed to unravel the complex mechanisms underlying this impairment. Our review highlights the essential role of computational models in providing detailed insights beyond clinical observations, enabling a deeper understanding of the cardiovascular system. By elucidating the existing models of the heart (3D, 2D, and 0D), cardiac valves, and blood vessels (3D, 1D, and 0D), as well as discussing mechanical boundary conditions, model parameterization and validation, coupling approaches, computer resources and diverse applications, we establish a comprehensive overview of the field. The descriptions as well as the pros and cons on the choices of different dimensionality in heart, valve, and circulation are provided. Crucially, we emphasize the significance of evaluating heart-vessel interaction in pathological conditions and propose future research directions, such as the development of fully coupled personalized multidimensional models, integration of deep learning techniques, and comprehensive assessment of confounding effects on biomarkers.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"24"},"PeriodicalIF":2.9,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10885508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139929903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-invasive parameters of autonomic function using beat-to-beat cardiovascular variations and arterial stiffness in hypertensive individuals: a systematic review.","authors":"Jia Hui Ooi, Renly Lim, Hansun Seng, Maw Pin Tan, Choon Hian Goh, Nigel H Lovell, Ahmadreza Argha, Hooi Chin Beh, Nor Ashikin Md Sari, Einly Lim","doi":"10.1186/s12938-024-01202-6","DOIUrl":"10.1186/s12938-024-01202-6","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;Non-invasive, beat-to-beat variations in physiological indices provide an opportunity for more accessible assessment of autonomic dysfunction. The potential association between the changes in these parameters and arterial stiffness in hypertension remains poorly understood. This systematic review aims to investigate the association between non-invasive indicators of autonomic function based on beat-to-beat cardiovascular signals with arterial stiffness in individuals with hypertension.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Four electronic databases were searched from inception to June 2022. Studies that investigated non-invasive parameters of arterial stiffness and autonomic function using beat-to-beat cardiovascular signals over a period of &gt; 5min were included. Study quality was assessed using the STROBE criteria. Two authors screened the titles, abstracts, and full texts independently.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Nineteen studies met the inclusion criteria. A comprehensive overview of experimental design for assessing autonomic function in terms of baroreflex sensitivity and beat-to-beat cardiovascular variabilities, as well as arterial stiffness, was presented. Alterations in non-invasive indicators of autonomic function, which included baroreflex sensitivity, beat-to-beat cardiovascular variabilities and hemodynamic changes in response to autonomic challenges, as well as arterial stiffness, were identified in individuals with hypertension. A mixed result was found in terms of the association between non-invasive quantitative autonomic indices and arterial stiffness in hypertensive individuals. Nine out of 12 studies which quantified baroreflex sensitivity revealed a significant association with arterial stiffness parameters. Three studies estimated beat-to-beat heart rate variability and only one study reported a significant relationship with arterial stiffness indices. Three out of five studies which studied beat-to-beat blood pressure variability showed a significant association with arterial structural changes. One study revealed that hemodynamic changes in response to autonomic challenges were significantly correlated with arterial stiffness parameters.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The current review demonstrated alteration in autonomic function, which encompasses both the sympathetic and parasympathetic modulation of sinus node function and vasomotor tone (derived from beat-to-beat cardiovascular signals) in hypertension, and a significant association between some of these parameters with arterial stiffness. By employing non-invasive measurements to monitor changes in autonomic function and arterial remodeling in individuals with hypertension, we would be able to enhance our ability to identify individuals at high risk of cardiovascular disease. Understanding the intricate relationships among these cardiovascular variability measures and arterial stiffness could contribute toward better individualized t","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"23"},"PeriodicalIF":2.9,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139911935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kinematic difference and asymmetries during level walking in adolescent patients with different types of mild scoliosis.","authors":"Run Ji, Xiaona Liu, Yang Liu, Bin Yan, Jiemeng Yang, Wayne Yuk-Wai Lee, Ling Wang, Chunjing Tao, Shengzheng Kuai, Yubo Fan","doi":"10.1186/s12938-024-01211-5","DOIUrl":"10.1186/s12938-024-01211-5","url":null,"abstract":"<p><strong>Background: </strong>Adolescent idiopathic scoliosis (AIS), three-dimensional spine deformation, affects body motion. Previous research had indicated pathological gait patterns of AIS. However, the impact of the curve number on the walking mechanism has not been established. Therefore, this study aimed to compare the gait symmetry and kinematics in AIS patients with different curve numbers to healthy control.</p><p><strong>Results: </strong>In the spinal region, double curves AIS patients demonstrated a smaller sagittal symmetry angle (SA) and larger sagittal convex ROM of the trunk and lower spine than the control group. In the lower extremities, the single curve patients showed a significantly reduced SA of the knee joint in the frontal plane, while the double curves patients showed a significantly reduced SA of the hip in the transverse plane.</p><p><strong>Conclusion: </strong>The curve number indeed affects gait symmetry and kinematics in AIS patients. The double curves patients seemed to adopt a more \"careful walking\" strategy to compensate for the effect of spinal deformation on sensory integration deficits. This compensation mainly occurred in the sagittal plane. Compared to double curves patients, single curve patients unitized a similar walking strategy with healthy subjects.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"22"},"PeriodicalIF":3.9,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10875845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139899313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer","authors":"Arnab Barua, Xianta Jiang, Daniel Fuller","doi":"10.1186/s12938-024-01213-3","DOIUrl":"https://doi.org/10.1186/s12938-024-01213-3","url":null,"abstract":"Human activity Recognition (HAR) using smartphone sensors suffers from two major problems: sensor orientation and placement. Sensor orientation and sensor placement problems refer to the variation in sensor signal for a particular activity due to sensors’ altering orientation and placement. Extracting orientation and position invariant features from raw sensor signals is a simple solution for tackling these problems. Using few heuristic features rather than numerous time-domain and frequency-domain features offers more simplicity in this approach. The heuristic features are features which have very minimal effects of sensor orientation and placement. In this study, we evaluated the effectiveness of four simple heuristic features in solving the sensor orientation and placement problems using a 1D-CNN–LSTM model for a data set consisting of over 12 million samples. We accumulated data from 42 participants for six common daily activities: Lying, Sitting, Walking, and Running at 3-Metabolic Equivalent of Tasks (METs), 5-METs and 7-METs from a single accelerometer sensor of a smartphone. We conducted our study for three smartphone positions: Pocket, Backpack and Hand. We extracted simple heuristic features from the accelerometer data and used them to train and test a 1D-CNN–LSTM model to evaluate their effectiveness in solving sensor orientation and placement problems. We performed intra-position and inter-position evaluations. In intra-position evaluation, we trained and tested the model using data from the same smartphone position, whereas, in inter-position evaluation, the training and test data was from different smartphone positions. For intra-position evaluation, we acquired 70–73% accuracy; for inter-position cases, the accuracies ranged between 59 and 69%. Moreover, we performed participant-specific and activity-specific analyses. We found that the simple heuristic features are considerably effective in solving orientation problems. With further development, such as fusing the heuristic features with other methods that eliminate placement issues, we can also achieve a better result than the outcome we achieved using the heuristic features for the sensor placement problem. In addition, we found the heuristic features to be more effective in recognizing high-intensity activities.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"30 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"StairNet: visual recognition of stairs for human-robot locomotion.","authors":"Andrew Garrett Kurbis, Dmytro Kuzmenko, Bogdan Ivanyuk-Skulskiy, Alex Mihailidis, Brokoslaw Laschowski","doi":"10.1186/s12938-024-01216-0","DOIUrl":"10.1186/s12938-024-01216-0","url":null,"abstract":"<p><p>Human-robot walking with prosthetic legs and exoskeletons, especially over complex terrains, such as stairs, remains a significant challenge. Egocentric vision has the unique potential to detect the walking environment prior to physical interactions, which can improve transitions to and from stairs. This motivated us to develop the StairNet initiative to support the development of new deep learning models for visual perception of real-world stair environments. In this study, we present a comprehensive overview of the StairNet initiative and key research to date. First, we summarize the development of our large-scale data set with over 515,000 manually labeled images. We then provide a summary and detailed comparison of the performances achieved with different algorithms (i.e., 2D and 3D CNN, hybrid CNN and LSTM, and ViT networks), training methods (i.e., supervised learning with and without temporal data, and semi-supervised learning with unlabeled images), and deployment methods (i.e., mobile and embedded computing), using the StairNet data set. Finally, we discuss the challenges and future directions. To date, our StairNet models have consistently achieved high classification accuracy (i.e., up to 98.8%) with different designs, offering trade-offs between model accuracy and size. When deployed on mobile devices with GPU and NPU accelerators, our deep learning models achieved inference speeds up to 2.8 ms. In comparison, when deployed on our custom-designed CPU-powered smart glasses, our models yielded slower inference speeds of 1.5 s, presenting a trade-off between human-centered design and performance. Overall, the results of numerous experiments presented herein provide consistent evidence that StairNet can be an effective platform to develop and study new deep learning models for visual perception of human-robot walking environments, with an emphasis on stair recognition. This research aims to support the development of next-generation vision-based control systems for robotic prosthetic legs, exoskeletons, and other mobility assistive technologies.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"20"},"PeriodicalIF":3.9,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10870468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139740298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of using a depth camera or pressure mat for visual feedback balance training with functional electrical stimulation.","authors":"Derrick Lim, William Pei, Jae W Lee, Kristin E Musselman, Kei Masani","doi":"10.1186/s12938-023-01191-y","DOIUrl":"10.1186/s12938-023-01191-y","url":null,"abstract":"<p><p>Individuals with incomplete spinal-cord injury/disease are at an increased risk of falling due to their impaired ability to maintain balance. Our research group has developed a closed-loop visual-feedback balance training (VFBT) system coupled with functional electrical stimulation (FES) for rehabilitation of standing balance (FES + VFBT system); however, clinical usage of this system is limited by the use of force plates, which are expensive and not easily accessible. This study aimed to investigate the feasibility of a more affordable and accessible sensor such as a depth camera or pressure mat in place of the force plate. Ten able-bodied participants (7 males, 3 females) performed three sets of four different standing balance exercises using the FES + VFBT system with the force plate. A depth camera and pressure mat collected centre of mass and centre of pressure data passively, respectively. The depth camera showed higher Pearson's correlation (r > 98) and lower root mean squared error (RMSE < 10 mm) than the pressure mat (r > 0.82; RMSE < 4.5 mm) when compared with the force plate overall. Stimulation based on the depth camera showed lower RMSE than that based on the pressure mat relative to the FES + VFBT system. The depth camera shows potential as a replacement sensor to the force plate for providing feedback to the FES + VFBT system.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"19"},"PeriodicalIF":3.9,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10863251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139721474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Screening ovarian cancer by using risk factors: machine learning assists.","authors":"Raoof Nopour","doi":"10.1186/s12938-024-01219-x","DOIUrl":"10.1186/s12938-024-01219-x","url":null,"abstract":"<p><strong>Background and aim: </strong>Ovarian cancer (OC) is a prevalent and aggressive malignancy that poses a significant public health challenge. The lack of preventive strategies for OC increases morbidity, mortality, and other negative consequences. Screening OC through risk prediction could be leveraged as a powerful strategy for preventive purposes that have not received much attention. So, this study aimed to leverage machine learning approaches as predictive assistance solutions to screen high-risk groups of OC and achieve practical preventive purposes.</p><p><strong>Materials and methods: </strong>As this study is data-driven and retrospective in nature, we leveraged 1516 suspicious OC women data from one concentrated database belonging to six clinical settings in Sari City from 2015 to 2019. Six machine learning (ML) algorithms, including XG-Boost, Random Forest (RF), J-48, support vector machine (SVM), K-nearest neighbor (KNN), and artificial neural network (ANN) were leveraged to construct prediction models for OC. To choose the best model for predicting OC, we compared various prediction models built using the area under the receiver characteristic operator curve (AU-ROC).</p><p><strong>Results: </strong>Current experimental results revealed that the XG-Boost with AU-ROC = 0.93 (0.95 CI = [0.91-0.95]) was recognized as the best-performing model for predicting OC.</p><p><strong>Conclusions: </strong>ML approaches possess significant predictive efficiency and interoperability to achieve powerful preventive strategies leveraging OC screening high-risk groups.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"18"},"PeriodicalIF":3.9,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10863117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139721475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}