Intelligent medicine最新文献

英文中文

Improving vertebral diagnosis in computed tomography scans: a clinically oriented attention-driven asymmetric convolution network for segmentation

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2024-11-01 DOI: 10.1016/j.imed.2024.02.002

Bo Wang , Ruijie Wang , Zongren Chen , Qixiang Zhang , Wan Yuwen , Xia Liu

Objective

Vertebral segmentation in computed tomography (CT) images remains an essential issue in medical image analysis, stemming from the variability in vertebral shapes, high complex deformations, and the inherent ambiguity in CT scans. The purpose of this study was to develop advanced methods to effectively address this challenging task.

Methods

We proposed an attention-driven asymmetric convolution deep learning (AACDL) framework for vertebral segmentation. Specifically, our approach involved constructing a novel asymmetric convolutional U-shaped deep learning architecture to enhance the feature extraction capabilities by increasing its depth for capturing richer spatial details. Further, we constructed a pyramid global context module that integrates global context information through pyramid pooling to boost segmentation accuracy particularly in smaller anatomical regions. Sequential channel and spatial attention mechanisms were also implemented within the network to enable it to automatically concentrate on learning the most salient features and regions across different dimensions.

Results

The performance precision of our network was rigorously assessed using a suite of four benchmark metrics: the dice coefficient, mean intersection over union (mIoU), precision rate, and F1-score. When compared against the ground truth, our model delivered outstanding scores, attaining a dice coefficient of 82.79%, an mIoU of 90.72%, a precision rate of 90.19%, and an F1-score of 90.09%, each reflecting the commendable accuracy and reliability of our network's segmentation output.

Conclusion

The proposed AACDL method might successfully realize accurate segmentation of vertebral CT images, thereby demonstrating significant potential for clinical applications with its robust performance metrics. Its ability to handle the complexities associated with vertebral segmentation may pave the way for enhanced diagnostic and treatment planning processes in healthcare settings.

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引用次数: 0

Blockchain for digital healthcare: Case studies and adoption challenges

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2024-11-01 DOI: 10.1016/j.imed.2024.09.001

Fei Zhou , Yue Huang , Chengquan Li , Xiaobin Feng , Wei Yin , Guoyan Zhang , Sisi Duan

The healthcare industry is significantly transforming toward digital and smart healthcare. Blockchain, as an emerging distributed collaborative paradigm, offers a promising solution for ensuring trustworthiness and high availability of services in the evolving healthcare sector. This study aimed to provide a comprehensive survey of blockchain-based applications in smart healthcare. We first present the real-world blockchain use cases in smart healthcare and related fields, outlining the motivations for this study. Next, we review the cutting-edge blockchain applications in various domains, including health data sharing, public health management, drug supply chains, insurance claims, and the Internet-of-Medical-Things. A detailed analysis of several blockchain-based healthcare data sharing scenarios is also included. The findings illustrate the diverse applications of blockchain technology in enhancing healthcare systems, along with a detailed examination of the challenges related to technical implementation and adoption. We discussed the challenges encountered in blockchain integration in smart healthcare and propose potential solutions to guide future research in this area.

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引用次数: 0

Guide for Authors

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2024-11-01 DOI: 10.1016/S2667-1026(24)00078-0

引用次数: 0

Computing, data, and the role of general practitioners and general practice in England

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2024-11-01 DOI: 10.1016/j.imed.2024.04.001

Malcolm J. Fisk

This paper gave attention to two issues that arise because of the growth in the use of health data by general practitioners (GPs) and general practices in England. The issues were (a) the use and commercialisation of patients’ personal health data; and(b) the propensity of GPs and general practice staff, in utilising those data, to see patients as fragmented bodies rather than as ‘whole persons’. The paper included attention to the computing needs of general practice from the 1960s and notes the period of growth in GP computer use during the 1990s. The implications of recent increased computer use by GPs and general practices, as a contributor to the further scientification of health, were then explored. Significant is the fact that the paper finds consciousness, from the 1970s, of the two issues. Their importance was emphasised as the momentum increases around the utilisation and sharing of patient data. Related concerns about data privacy and confidentiality are highlighted. In this context, the paper recommended that further research be undertaken with urgency to explore the ways that caring relationships (that have been a hallmark of the work of GPs) can be safeguarded.

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引用次数: 0

Comparison of feature learning methods for non-invasive interstitial glucose prediction using wearable sensors in healthy cohorts: a pilot study

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2024-11-01 DOI: 10.1016/j.imed.2024.05.002

Xinyu Huang , Franziska Schmelter , Annemarie Uhlig , Muhammad Tausif Irshad , Muhammad Adeel Nisar , Artur Piet , Lennart Jablonski , Oliver Witt , Torsten Schröder , Christian Sina , Marcin Grzegorzek

Background

Alterations in glucose metabolism, especially the postprandial glucose response (PPGR), are crucial contributors to metabolic dysfunction, which underlies the pathogenesis of metabolic syndrome. Personalized low-glycemic diets have shown promise in reducing postprandial glucose spikes. However, current methods such as invasive continuous glucose monitoring (CGM) or multi-omics data integration to assess PPGR have limitations, including cost and invasiveness that hinder the widespread adoption of these methods in primary disease prevention. Our aim was to assess machine learning algorithms for predicting individual PPGR using non-invasive wearable devices, thereby, circumventing the limitations associated with the existing approaches. By identifying the most accurate model, we sought to provide a more accessible and efficient method for managing glucose metabolic dysfunction.

Methods

This data-driven analysis used the experimental dataset from the SENSE (”Systemische Ernährungsmedizin”) study. Healthy participants used an Empatica E4 wristband and Abbott Freestyle Libre 3 CGM for 10 days. Blood volume pulse, electrodermal activity, heart rate, skin temperature, and the corresponding CGM values were measured. Subsequently, four data-driven deep learning (DL) models-convolutional neural network, lightweight transformer, long short-term memory with attention, and Bi-directional LSTM (BiLSTM) were implemented and compared to determine the potential of DL in predicting interstitial glucose levels without involving food and activity logs.

Results

The proposed BiLSTM achieved the best interstitial glucose prediction performance, with an average root mean squared error of 13.42 mg/dL, an average mean absolute percentage error of 0.12, and only 3.01% values falling within area D in Clarke error grid analysis, incorporating the leave-one-out cross-validation strategy for a five-minute prediction horizon.

Conclusion

The findings of this study may demonstrate the feasibility of transferring knowledge gained from invasive glucose monitoring devices to non-invasive approaches. Furthermore, it could emphasize the promising prospects of combining DL with wearable technologies to predict glucose levels in healthy individuals.

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引用次数: 0

Challenges in standardizing image quality across diverse ultrasound devices

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2024-11-01 DOI: 10.1016/j.imed.2024.01.002

Rebeca Tenajas , David Miraut

引用次数: 0

Application of statistical shape models in orthopedics: a narrative review

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2024-11-01 DOI: 10.1016/j.imed.2024.05.001

Xingbo Cai , Ying Wu , Junshen Huang , Long Wang , Yongqing Xu , Sheng Lu

Statistical shape models (SSMs) are effective for image processing and analysis and have been used in various medical fields, including face recognition and cranial bone recognition. In orthopedics, SSMs are being used in numerous applications, such as automated segmentation of medical images, preoperative planning, intraoperative navigation combined with robotics, simulation reconstruction of defects, human biomechanics research, description of anatomical shape changes, and prosthesis design. This review highlighted the wide range of applications while acknowledging the diversity of methodologies and techniques encompassed by SSMs, including Gaussian process models and nonlinear solutions. In addition, the available software packages for constructing shape models, such as Scalismo, ShapeWorks, and Deformetrica, were discussed.

引用次数: 0

Few-shot learning based histopathological image classification of colorectal cancer

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2024-11-01 DOI: 10.1016/j.imed.2024.05.003

Rui Li , Xiaoyan Li , Hongzan Sun , Jinzhu Yang , Md Rahaman , Marcin Grzegozek , Tao Jiang , Xinyu Huang , Chen Li

Background

Colorectal cancer is a prevalent and deadly disease worldwide, posing significant diagnostic challenges. Traditional histopathologic image classification is often inefficient and subjective. Although some histopathologists use computer-aided diagnosis to improve efficiency, these methods depend heavily on extensive data and specific annotations, limiting their development. To address these challenges, this paper proposes a method based on few-shot learning.

Methods

This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant categories. The model comprises modules for feature extraction, dimensionality reduction, and classification, trained using a combination of contrast loss and cross-entropy loss. In this paper, we detailed the setup of hyperparameters:

n

-way,

k

-shot,

β

, and the creation of support, query, and test datasets.

Results

Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per category. We documented the model’s loss, accuracy, and the confusion matrix of the results. Additionally, we employed the

t

-SNE algorithm to analyze and assess the model’s classification performance.

Conclusion

The proposed model may demonstrate significant advantages in accuracy and minimal data dependency, performing robustly across all tested

n

-way,

k

-shot scenarios. It consistently achieved over 93% accuracy on comprehensive test datasets, including 1916 samples, confirming its high classification accuracy and strong generalization capability. Our research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare, difficult-to-diagnose cases.

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引用次数: 0

Increasing the accuracy and reproducibility of positron emission tomography radiomics for predicting pelvic lymph node metastasis in patients with cervical cancer using 3D local binary pattern-based texture features 利用基于三维局部二元模式的纹理特征提高正电子发射断层扫描放射组学预测宫颈癌患者盆腔淋巴结转移的准确性和可重复性

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2024-08-01 DOI: 10.1016/j.imed.2024.03.001

Yang Yu , Xiaoran Li , Tianming Du , Md Rahaman , Marcin Jerzy Grzegorzek , Chen Li , Hongzan Sun

Background

The reproducibility of positron emission tomography (PET) radiomics features is affected by several factors, such as scanning equipment, drug metabolism time and reconstruction algorithm. We aimed to explore the role of 3D local binary pattern (LBP)-based texture in increasing the accuracy and reproducibility of PET radiomics for predicting pelvic lymph node metastasis (PLNM) in patients with cervical cancer.

Methods

We retrospectively analysed data from 177 patients with cervical squamous cell carcinoma. They underwent ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG)whole-body PET/computed tomography (PET/CT), followed by pelvic ¹⁸F-FDG PET/magnetic resonance imaging (PET/MR). We selected reproducible and informative PET radiomics features using Lin's concordance correlation coefficient, least absolute shrinkage and selection operator algorithm, and established 4 models, PET/CT, PET/CT-fusion, PET/MR and PET/MR-fusion, using the logistic regression algorithm. We performed receiver operating characteristic (ROC) curve analysis to evaluate the models in the training data set (65 patients who underwent radical hysterectomy and pelvic lymph node dissection) and test data set (112 patients who received concurrent chemoradiotherapy or no treatment). The DeLong test was used for pairwise comparison of the ROC curves among the models.

Results

The distribution of age, squamous cell carcinoma (SCC), International Federation of Gynaecology and Obstetrics stage and PLNM between the training and test data sets were different (P < 0.05). The LBP-transformed radiomics features (50/379) had higher reproducibility than the original radiomics features (9/107). Accuracy of each model in predicting PLNM was as follows: training data set: PET/CT = PET/CT-fusion = PET/MR-fusion (0.848) and test data set: PET/CT = PET/CT-fusion (0.985) > PET/MR = PET/MR-fusion (0.954). There was no statistical difference between the ROC curve of PET/CT and PET/MR models in both data sets (P > 0.05).

Conclusions

The LBP-transformed radiomics features based on PET images could increase the accuracy and reproducibility of PET radiomics in predicting pelvic lymph node metastasis in cervical cancer to allow the model to be generalised for clinical use across multiple centres.

背景正电子发射断层扫描（PET）放射组学特征的可重复性受多种因素的影响，如扫描设备、药物代谢时间和重建算法。我们的目的是探索基于三维局部二元模式（LBP）的纹理在提高正电子发射计算机断层成像预测宫颈癌患者盆腔淋巴结转移（PLNM）的准确性和可重复性方面的作用。他们接受了18F-氟脱氧葡萄糖（18F-FDG）全身正电子发射计算机断层扫描（PET/CT），然后进行了盆腔18F-FDG正电子发射计算机断层扫描/磁共振成像（PET/MR）。我们使用林氏一致性相关系数、最小绝对缩减和选择算子算法选择了可重复和有信息量的 PET 放射组学特征，并使用逻辑回归算法建立了 PET/CT、PET/CT-融合、PET/MR 和 PET/MR- 融合 4 个模型。我们对训练数据集（65 名接受根治性子宫切除术和盆腔淋巴结清扫术的患者）和测试数据集（112 名同时接受化放疗或未接受任何治疗的患者）进行了接收者操作特征（ROC）曲线分析，以评估模型。结果训练数据集和测试数据集的年龄、鳞状细胞癌（SCC）、国际妇产科联盟分期和 PLNM 的分布不同（P < 0.05）。经 LBP 转换的放射组学特征（50/379）比原始放射组学特征（9/107）具有更高的可重复性。每个模型预测 PLNM 的准确性如下：训练数据集：PET/CT = PET/CT-fusion = PET/MR-fusion (0.848)，测试数据集：PET/CT = PET/CT-fusion (0.985) > PET/MR = PET/MR-fusion (0.954)。结论基于PET图像的LBP变换放射组学特征可提高PET放射组学预测宫颈癌盆腔淋巴结转移的准确性和可重复性，从而使该模型在多个中心的临床应用中得到推广。

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引用次数: 0

Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging 从临床磁共振成像图像中自动分割人脑组织，改进神经学诊断和治疗规划

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2024-08-01 DOI: 10.1016/j.imed.2023.10.001

Puranam Revanth Kumar , Rajesh Kumar Jha , P Akhendra Kumar , B Deevena Raju

Objective

Segmentation of medical images is a crucial process in various image analysis applications. Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image analysis. One of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging (MRI) data. It allows for precise quantitative examination of the brain, which aids in diagnosis, identification, and classification of disorders. Consequently, the efficacy of the segmentation approach is crucial to disease diagnosis and treatment planning.

Methods

This study presented a hybrid optimization method for segmenting brain tissue in clinical MRI scans using a fractional Henry horse herd gas optimization-based Shepard convolutional neural network (FrHHGO-based ShCNN). To segment the clinical brain MRI images into white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) tissues, the proposed framework was evaluated on the Lifespan Human Connectome Projects (HCP) database. The hybrid optimization algorithm, FrHHGO, integrates the fractional Henry gas optimization (FHGO) and horse herd optimization (HHO) algorithms. Training required 30 min, whereas testing and segmentation of brain tissues from an unseen image required an average of 12 s.

Results

Compared to the results obtained with no refinements, the Skull stripping refinement showed significant improvement. As the method included a preprocessing stage, it was flexible enough to enhance image quality, allowing for better results even with low-resolution input. Maximum precision of 93.2%, recall of 91.5%, Dice score of 91.1%, and F1-score of 90.5% were achieved using the proposed FrHHGO-based ShCNN, which was superior to all other approaches.

Conclusion

The proposed method may outperform existing state-of-the-art methodologies in qualitative and quantitative measurements across a wide range of medical modalities. It might demonstrate its potential for real-life clinical application.

目标医学图像的分割是各种图像分析应用中的一个关键过程。在医学图像分析中，与手动分割相比，自动分割方法的准确性更胜一筹。利用临床获得的磁共振成像（MRI）数据进行脑组织自动分割是对大脑进行定量分析的重要阶段之一。它可以对大脑进行精确的定量检查，有助于疾病的诊断、识别和分类。因此，分割方法的有效性对疾病诊断和治疗计划至关重要。本研究提出了一种混合优化方法，利用基于分数亨利马群气体优化的 Shepard 卷积神经网络（FrHHGO-based ShCNN）分割临床 MRI 扫描中的脑组织。为了将临床脑部核磁共振成像图像分割为白质（WM）、灰质（GM）和脑脊液（CSF）组织，研究人员在Lifespan Human Connectome Projects（HCP）数据库上对所提出的框架进行了评估。混合优化算法 FrHHGO 整合了分数亨利气体优化（FHGO）和马群优化（HHO）算法。训练需要 30 分钟，而测试和从未曾见过的图像中分割脑组织平均需要 12 秒。由于该方法包括一个预处理阶段，因此在提高图像质量方面具有足够的灵活性，即使在输入低分辨率图像时也能获得更好的结果。使用所提出的基于 FrHHGO 的 ShCNN，精确度达到 93.2%，召回率达到 91.5%，Dice 分数达到 91.1%，F1 分数达到 90.5%，优于所有其他方法。它可以证明其在现实生活中的临床应用潜力。

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