Intelligence-based medicine最新文献_第7页

Optimizing ResNet50 performance using stochastic gradient descent on MRI images for Alzheimer's disease classification 在MRI图像上使用随机梯度下降优化ResNet50性能，用于阿尔茨海默病分类

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-01-30 DOI: 10.1016/j.ibmed.2025.100219

Mohamed Amine Mahjoubi , Driss Lamrani , Shawki Saleh , Wassima Moutaouakil , Asmae Ouhmida , Soufiane Hamida , Bouchaib Cherradi , Abdelhadi Raihani

The field of optimization is focused on the formulation, analysis, and resolution of problems involving the minimization or maximization of functions. A particular subclass of optimization problems, known as empirical risk minimization, involves fitting a model to observed data. These problems play a central role in various areas such as machine learning, statistical modeling, and decision theory, where the objective is to find a model that best approximates underlying patterns in the data by minimizing a specified loss or risk function. In deep learning (DL) systems, various optimization algorithms are utilized with the gradient descent (GD) algorithm being one of the most significant and effective. Research studies have improved the GD algorithm and developed various successful variants, including stochastic gradient descent (SGD) with momentum, AdaGrad, RMSProp, and Adam. This article provides a comparative analysis of these stochastic gradient descent algorithms based on their accuracy, loss, and training time, as well as the loss of each algorithm in generating an optimization solution. Experiments were conducted using Transfer Learning (TL) technique based on the pre-trained ResNet50 base model for image classification, with a focus on stochastic gradient (SG) for performances optimization. The case study in this work is based on a data extract from the Alzheimer's image dataset, which contains four classes such as Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented. The obtained results with the Adam and SGD momentum optimizers achieved the highest accuracy of 97.66 % and 97.58 %, respectively.

优化领域的重点是制定、分析和解决涉及函数的最小化或最大化的问题。优化问题的一个特殊子类，被称为经验风险最小化，涉及到将模型拟合到观测数据。这些问题在机器学习、统计建模和决策理论等各个领域发挥着核心作用，这些领域的目标是通过最小化指定的损失或风险函数来找到最接近数据中潜在模式的模型。在深度学习（DL）系统中，有各种各样的优化算法，其中梯度下降（GD）算法是最重要和最有效的算法之一。研究改进了GD算法，并开发了各种成功的变体，包括带动量的随机梯度下降（SGD）、AdaGrad、RMSProp和Adam。本文根据这些随机梯度下降算法的精度、损失和训练时间，以及每种算法在生成优化解时的损失，对它们进行了比较分析。基于预训练的ResNet50基模型，采用迁移学习（TL）技术进行图像分类实验，重点采用随机梯度（SG）进行性能优化。本研究的案例研究基于阿尔茨海默病图像数据集的数据提取，该数据集包含轻度痴呆、中度痴呆、非痴呆和极轻度痴呆等四类。使用Adam动量优化器和SGD动量优化器获得的结果准确率最高，分别为97.66%和97.58%。

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引用次数: 0

Image-based machine learning model as a tool for classification of [18F]PR04.MZ PET images in patients with parkinsonian syndrome 将基于图像的机器学习模型作为帕金森综合征患者[18F]PR04.MZ PET 图像分类的工具

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-03-04 DOI: 10.1016/j.ibmed.2025.100232

Maria Jiménez , Cristian Soza-Ried , Vasko Kramer , Sebastian A. Ríos , Arlette Haeger , Carlos Juri , Horacio Amaral , Pedro Chana-Cuevas

Parkinsonian syndrome (PS) is characterized by bradykinesia, resting tremor, rigidity, and encapsulates the clinical manifestation observed in various neurodegenerative disorders. Positron emission tomography (PET) imaging plays an important role in diagnosing PS by detecting the progressive loss of dopaminergic neurons. This study aimed to develop and compare five machine-learning models for the automatic classification of 204 [¹⁸F]PR04.MZ PET images, distinguishing between patients with PS and subjects without clinical evidence for dopaminergic deficit (SWEDD). Previously analyzed and classified by three expert blind readers into PS compatible (1) and SWEDDs (0), the dataset was processed in both two-dimensional and three-dimensional formats. Five widely used pattern recognition algorithms were trained and validated their performance. These algorithms were compared against the majority reading of expert diagnosis, considered the gold standard. Comparing the accuracy of 2D and 3D format images suggests that, without the depth dimension, a single image may overemphasize specific regions. Overall, three models outperformed with an accuracy greater than 98 %, demonstrating that machine-learning models trained with [¹⁸F]PR04.MZ PET images can provide a highly accurate and precise tool to support clinicians in automatic PET image analysis. This approach may be a first step in reducing the time required for interpretation, as well as increase certainty in the diagnostic process.

帕金森综合征（Parkinsonian Syndrome，PS）以运动迟缓、静止性震颤和僵直为特征，是各种神经退行性疾病的临床表现。正电子发射断层扫描（PET）成像通过检测多巴胺能神经元的逐渐丧失，在诊断帕金森综合征中发挥着重要作用。本研究旨在开发和比较五种机器学习模型，用于对204张[18F]PR04.MZ PET图像进行自动分类，区分PS患者和无多巴胺能缺失临床证据的受试者（SWEDD）。该数据集之前由三位盲人专家进行了分析和分类，分为 PS 相容性（1）和 SWEDD（0），并以二维和三维格式进行了处理。对五种广泛使用的模式识别算法进行了训练，并对其性能进行了验证。这些算法与被视为金标准的专家诊断的多数读数进行了比较。比较二维和三维格式图像的准确性表明，如果没有深度维度，单一图像可能会过分强调特定区域。总体而言，三个模型的准确率都超过了 98%，这表明使用[18F]PR04.MZ PET 图像训练的机器学习模型可以提供一种高度准确和精确的工具，为临床医生自动 PET 图像分析提供支持。这种方法可能是减少判读所需时间的第一步，并能提高诊断过程的确定性。

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引用次数: 0

Enhancing emotion recognition through multi-modal data fusion and graph neural networks 通过多模态数据融合和图神经网络增强情绪识别

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-08-22 DOI: 10.1016/j.ibmed.2025.100291

Kasthuri Devarajan , Suresh Ponnan , Sundresan Perumal

In this paper, a novel emotion detection system is proposed based on Graph Neural Network (GNN) architecture, which is used to integrate and learn from multiple data sets (EEG, face expression, physiological signals). The proposed GNN is able to learn about interactions between multiple modalities, so as to extract a single picture of emotion categorization. This model is very good and gets 91.25 % accuracy, 91.26 % precision, 91.25 % recall and 91.25 % F1-score. Moreover, the proposed GNN is a sensible trade-off between speed and precision, with a calculation time of 163 ms. The Proposed GNN is better, primarily due to its ability to represent complex relations between multi-modal inputs, thereby improving its real-time emotional state recognition and classification performance. The proposed GNN demonstrates its suitability for powerful emotion detection by outperforming all models in classification precision and multi-modal data fusion, surpassing traditional models such as SVM, KNN, CCA, CNN, and RNN. The Proposed GNN consistently proves to be the most accurate and robust solution, having been the most dominant technique in emotion detection, despite CNN and RNN achieving slightly better results.

本文提出了一种基于图神经网络（GNN）架构的情绪检测系统，该系统用于对多个数据集（EEG、面部表情、生理信号）进行整合和学习。提出的GNN能够学习多个模态之间的相互作用，从而提取出单一的情绪分类图像。该模型的准确率为91.25%，精密度为91.26%，召回率为91.25%，f1分数为91.25%。此外，所提出的GNN在速度和精度之间进行了合理的权衡，计算时间为163 ms。提出的GNN更好，主要是因为它能够表示多模态输入之间的复杂关系，从而提高了其实时情绪状态识别和分类性能。本文提出的GNN在分类精度和多模态数据融合方面优于所有模型，超越了SVM、KNN、CCA、CNN和RNN等传统模型，证明了它适合于强大的情感检测。尽管CNN和RNN取得了稍好的结果，但所提出的GNN始终被证明是最准确和鲁棒的解决方案，是情绪检测中最主要的技术。

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引用次数: 0

Clinical-ready CNN framework for lung cancer classification: Systematic optimization for healthcare deployment with enhanced computational efficiency 用于肺癌分类的临床就绪CNN框架：提高计算效率的医疗部署系统优化

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-08-21 DOI: 10.1016/j.ibmed.2025.100292

G. Inbasakaran, J. Anitha Ruth

Purpose

This study develops a computationally efficient Convolutional Neural Network (CNN) for lung cancer classification in Computed Tomography (CT) images, addressing the critical need for accurate diagnostic tools deployable in resource-constrained clinical settings.

Methods

Using the IQ-OTH/NCCD dataset (1190 CT images: normal, benign, and malignant classes from 110 patients), we implemented systematic architecture optimization with strategic data augmentation to address class imbalance and limited dataset challenges. Patient-level data splitting prevented leakage, ensuring valid performance metrics. The model was evaluated using 5-fold cross-validation and compared against established architectures using McNemar's test for statistical significance.

Results

The optimized CNN achieved 94 % classification accuracy with only 4.2 million parameters and 18 ms inference time. Performance significantly exceeded AlexNet (85 %), VGG-16 (88 %), ResNet-50 (90 %), InceptionV3 (87 %), and DenseNet (86 %) with p < 0.05. Malignant case detection showed excellent clinical metrics (precision: 0.96, recall: 0.95, F1-score: 0.95), critical for minimizing false negatives. Ablation studies revealed data augmentation contributed 6.6 % accuracy improvement, with rotation and translation proving most effective. The model operates 4.3 × faster than ResNet-50 while using 6 × fewer parameters, enabling deployment on standard clinical workstations with 4–8 GB GPU memory.

Conclusions

Carefully optimized CNN architectures can achieve superior diagnostic performance while meeting computational constraints of real-world medical settings. Our approach demonstrates that systematic optimization strategies effectively balance accuracy with clinical deployment feasibility, providing a practical framework for implementing AI-assisted lung cancer detection in resource-limited healthcare environments. The model's high sensitivity for malignant cases positions it as a valuable clinical decision support tool.

目的：本研究开发了一种计算效率高的卷积神经网络（CNN），用于计算机断层扫描（CT）图像中的肺癌分类，解决了在资源有限的临床环境中部署准确诊断工具的关键需求。方法利用iqoth /NCCD数据集（来自110例患者的1190张CT图像：正常、良性和恶性分类），通过战略性数据增强实现系统架构优化，以解决分类不平衡和数据集有限的挑战。患者级数据分割防止了泄漏，确保了有效的性能指标。该模型使用5倍交叉验证进行评估，并使用McNemar的统计显著性检验与已建立的架构进行比较。结果优化后的CNN只需要420万个参数和18 ms的推理时间，分类准确率达到94%。性能显著高于AlexNet（85%）、VGG-16（88%）、ResNet-50（90%）、InceptionV3（87%）和DenseNet (86%), p < 0.05。恶性病例的检出表现出优异的临床指标（准确率：0.96，召回率：0.95，f1评分：0.95），这对于减少假阴性至关重要。消融研究显示，数据增强提高了6.6%的准确性，旋转和平移被证明是最有效的。该模型的运行速度比ResNet-50快4.3倍，同时使用的参数减少了6倍，可在具有4-8 GB GPU内存的标准临床工作站上部署。结论经过精心优化的CNN架构在满足现实医疗环境计算约束的情况下，能够取得优异的诊断性能。我们的方法表明，系统优化策略有效地平衡了准确性和临床部署的可行性，为在资源有限的医疗环境中实施人工智能辅助肺癌检测提供了一个实用的框架。该模型对恶性病例的高敏感性使其成为一种有价值的临床决策支持工具。

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引用次数: 0

Comparative analysis of deep learning and machine learning techniques for forecasting new malaria cases in Cameroon’s Adamaoua region 深度学习和机器学习技术在喀麦隆阿达马乌阿地区预测新疟疾病例的比较分析

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-02-10 DOI: 10.1016/j.ibmed.2025.100220

Esaie Naroum , Ebenezer Maka Maka , Hamadjam Abboubakar , Paul Dayang , Appolinaire Batoure Bamana , Benjamin Garga , Hassana Daouda Daouda , Mohsen Bakouri , Ilyas Khan

The Plasmodium parasite, which causes malaria is transmitted by Anopheles mosquitoes, and remains a major development barrier in Africa. This is particularly true considering the conducive environment that promotes the spread of malaria. This study examines several machine learning approaches, such as long short term memory (LSTM), random forests (RF), support vector machines (SVM), and data regularization models including Ridge, Lasso, and ElasticNet, in order to forecast the occurrence of malaria in the Adamaoua region of Cameroon. The LSTM, a recurrent neural network variant, performed the best with 76% accuracy and a low error rate (RMSE = 0.08). Statistical evidence indicates that temperatures exceeding 34 degrees halt mosquito vector reproduction, thereby slowing the spread of malaria. However, humidity increases the morbidity of the condition. The survey also identified high-risk areas in Ngaoundéré Rural and Urban and Meiganga. Between 2018 and 2022, the Adamaoua region had 20.1%, 12.3%, and 10.0% of malaria cases, respectively, in these locations. According to the estimate, the number of malaria cases in the Adamaoua region will rise gradually between 2023 and 2026, peaking in 2029 before declining in 2031.

引起疟疾的疟原虫是由按蚊传播的，它仍然是非洲的一个主要发展障碍。考虑到促进疟疾传播的有利环境，这一点尤其正确。本研究探讨了几种机器学习方法，如长短期记忆（LSTM）、随机森林（RF）、支持向量机（SVM）和数据正则化模型（包括Ridge、Lasso和ElasticNet），以预测喀麦隆阿达马瓦地区疟疾的发生。LSTM，一种循环神经网络变体，表现最好，准确率为76%，错误率低（RMSE = 0.08）。统计证据表明，超过34度的温度会阻止蚊子媒介的繁殖，从而减缓疟疾的传播。然而，湿度增加了病情的发病率。调查还确定了恩oundd农村和城市以及梅甘加的高风险地区。2018年至2022年期间，阿达马乌瓦地区分别占这些地区疟疾病例的20.1%、12.3%和10.0%。据估计，阿达马乌瓦地区的疟疾病例数将在2023年至2026年期间逐步上升，在2029年达到峰值，然后在2031年下降。

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引用次数: 0

PU-MLP: A PU-learning based method for polypharmacy side-effects detection based on multi-layer perceptron and feature extraction techniques PU-MLP：一种基于pu学习的基于多层感知器和特征提取技术的多药副作用检测方法

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-05-31 DOI: 10.1016/j.ibmed.2025.100265

Abedin Keshavarz, Amir Lakizadeh

Polypharmacy, or the concurrent use of multiple medications, increases the risk of adverse effects due to drug interactions. As polypharmacy becomes more prevalent, forecasting these interactions is essential in the pharmaceutical field. Due to the limitations of clinical trials in detecting rare side effects associated with polypharmacy, computational methods are being developed to model these adverse effects. This study introduces a method named PU-MLP, based on a Multi-Layer Perceptron, to predict side effects from drug combinations. This research utilizes advanced machine learning techniques to explore the connections between medications and their adverse effects. The approach consists of three key stages: first, it creates an optimal representation of each drug using a combination of a random forest classifier, Graph Neural Networks (GNNs), and dimensionality reduction techniques. Second, it employs Positive Unlabeled learning to address data uncertainty. Finally, a Multi-Layer Perceptron model is utilized to predict polypharmacy side effects. Performance evaluation using 5-fold cross-validation shows that the proposed method surpasses other approaches, achieving impressive scores of 0.99, 0.99, and 0.98 in AUPR, AUC, and F1 measures, respectively.

多种用药，或同时使用多种药物，由于药物相互作用，增加了不良反应的风险。随着多药制药变得越来越普遍，预测这些相互作用在制药领域是必不可少的。由于临床试验在检测与多种药物相关的罕见副作用方面的局限性，正在开发计算方法来模拟这些副作用。本研究提出了一种基于多层感知机的PU-MLP方法来预测药物组合的副作用。这项研究利用先进的机器学习技术来探索药物及其副作用之间的联系。该方法包括三个关键阶段：首先，它使用随机森林分类器、图神经网络（gnn）和降维技术的组合创建每种药物的最佳表示。其次，它采用正无标签学习来解决数据的不确定性。最后，利用多层感知器模型对多药副作用进行预测。使用5倍交叉验证的性能评估表明，所提出的方法优于其他方法，在AUPR、AUC和F1指标上分别取得了令人印象深刻的0.99、0.99和0.98的分数。

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引用次数: 0

Speech biomarkers predict amyloid status in cognitively unimpaired adults 语言生物标志物预测认知功能正常的成年人的淀粉样蛋白状态

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-10-14 DOI: 10.1016/j.ibmed.2025.100306

Peru Gabirondo , María García-Martínez , Ana Pozueta-Cantudo , Patricia Laura Maran , Patricia Dias , Tomas Rojo , Javier Jiménez-Raboso , Carmen Lage , Francisco Martínez-Dubarbie , Sara López-García , Marta Fernández-Matarrubia , Andrea Corrales-Pardo , María Bravo , Juan Irure-Ventura , Marcos López-Hoyos , Pascual Sánchez-Juan , Carla Zaldua , Eloy Rodríguez-Rodríguez

引用次数: 0

Uncertainty-aware hybrid optimization for robust cardiovascular disease detection: A clinical translation framework 不确定性感知混合优化稳健心血管疾病检测：临床翻译框架

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-10-09 DOI: 10.1016/j.ibmed.2025.100302

Tamanna Jena , Rahul Suryodai , Desidi Narsimha Reddy , Kambala Vijaya Kumar , Elangovan Muniyandy , N.V. Phani Sai Kumar

Background

Cardiovascular disease causes 17.9 million deaths annually, yet current AI systems achieve ∼82 % accuracy without uncertainty quantification—limiting clinical utility where prediction confidence directly guides life-saving treatment decisions.

Objective

We developed an uncertainty-aware hybrid optimization framework for robust CVD detection that provides clinicians with both risk predictions and confidence intervals, enabling personalized decision-making under real-world clinical conditions.

Methods

Our clinical translation framework integrates multiple complementary AI models (Gaussian processes, gradient-boosted trees, Transformers) through uncertainty-guided optimization. Key clinical innovations include: (1) real-time uncertainty calibration responding to data quality variations, (2) dynamic model weighting adapting to individual patient characteristics, and (3) interpretable confidence intervals supporting clinical decision protocols.

Results

Clinical validation on 12,458 CVD patients from MIMIC-III and UK Biobank demonstrated clinically significant improvements: +1.4 % AUC (0.853 vs 0.839, p < 0.01) translating to 50 additional correct diagnoses per 10,000 patients, +1.5 % balanced accuracy, and 20 % better uncertainty calibration. The framework maintained robust performance (>80 % AUC) under realistic clinical noise while providing reliable confidence intervals across all risk levels.

Clinical translation

This framework delivers immediate clinical utility through real-time inference (<2s), FHIR-compliant EHR integration, and physician-validated uncertainty interpretation. Implementation prevents an estimated 50 missed diagnoses and 23 unnecessary procedures per 10,000 patients screened annually.

Conclusions

Our uncertainty-aware framework represents the first clinically ready AI system providing both accurate CVD risk assessment and trustworthy confidence measures, directly addressing physician adoption barriers and supporting personalized cardiovascular care.

背景：心血管疾病每年导致1790万人死亡，但目前的人工智能系统在没有不确定性量化的情况下达到了82%的准确率，这限制了临床实用性，预测置信度直接指导挽救生命的治疗决策。目的：我们开发了一个不确定性感知的混合优化框架，用于稳健的心血管疾病检测，为临床医生提供风险预测和置信区间，从而在现实临床条件下实现个性化决策。方法通过不确定性导向优化，sour临床翻译框架集成了多个互补的人工智能模型（高斯过程、梯度增强树、变形金刚）。关键的临床创新包括：(1)响应数据质量变化的实时不确定度校准，(2)适应个体患者特征的动态模型加权，以及(3)支持临床决策方案的可解释置信区间。结果：来自MIMIC-III和UK Biobank的12,458例CVD患者的临床验证显示出临床显着改善：+ 1.4%的AUC （0.853 vs 0.839, p < 0.01）转化为每10,000例患者额外50例正确诊断，+ 1.5%的平衡准确性和20%的不确定度校准。该框架在真实的临床噪声下保持稳健的性能（80% AUC），同时在所有风险水平上提供可靠的置信区间。临床翻译该框架通过实时推理（<2s）、符合fhir的EHR集成和医生验证的不确定性解释，提供即时的临床效用。每年每1万名接受筛查的患者中，估计有50例漏诊和23例不必要的手术得到预防。结论我们的不确定性感知框架代表了第一个临床就绪的人工智能系统，提供准确的心血管疾病风险评估和可信赖的信心措施，直接解决医生采用障碍并支持个性化心血管护理。

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引用次数: 0

A comparison of techniques for predicting telehealth visit failure 预测远程医疗访问失败的技术比较

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-03-23 DOI: 10.1016/j.ibmed.2025.100235

Alexander J. Idarraga , David F. Schneider

Objective

Telehealth is an increasingly important method for delivering care. Health systems lack the ability to accurately predict which telehealth visits will fail due to poor connection, poor technical literacy, or other reasons. This results in wasted resources and disrupted patient care. The purpose of this study is to characterize and compare various methods for predicting telehealth visit failure, and to determine the prediction method most suited for implementation in a real-time operational setting.

Methods

A single-center, retrospective cohort study was conducted using data sourced from our data warehouse. Patient demographic information and data characterizing prior visit success and engagement with electronic health tools were included. Three main model types were evaluated: an existing scoring model developed by Hughes et al., a regression-based scoring model, and Machine Learning classifiers. Variables were selected for their importance and anticipated availability; Number Needed to Treat was used to demonstrate the number of interventions (e.g. pre-visit phone calls) required to improve success rates in the context of weekly patient volumes.

Results

217, 229 visits spanning 480 days were evaluated, of which 22,443 (10.33 %) met criteria for failure. Hughes et al.’s model applied to our data yielded an Area Under the Receiver Operating Characteristics Curve (AUC ROC) of 0.678 when predicting failure. A score-based model achieved an AUC ROC of 0.698. Logistic Regression, Random Forest, and Gradient Boosting models demonstrated AUC ROCs ranging from 0.7877 to 0.7969. A NNT of 32 was achieved if the 263 highest-risk patients were selected in a low-volume week using the RF classifier, compared to an expected NNT of 90 if the same number of patients were randomly selected.

Conclusions

Machine Learning classifiers demonstrated superiority over score-based methods for predicting telehealth visit failure. Prospective evaluation is required; evaluation using NNT as a metric can help to operationalize these models.

目的远程医疗是一种日益重要的医疗服务方式。卫生系统缺乏准确预测哪些远程医疗访问将由于连接不良、技术素养低下或其他原因而失败的能力。这导致资源浪费和病人护理中断。本研究的目的是描述和比较各种预测远程医疗访问失败的方法，并确定最适合在实时操作环境中实施的预测方法。方法采用单中心、回顾性队列研究，数据来源于我们的数据仓库。包括患者人口统计信息和表征先前访问成功和使用电子健康工具的数据。评估了三种主要的模型类型：Hughes等人开发的现有评分模型，基于回归的评分模型和机器学习分类器。根据变量的重要性和预期可用性选择变量；需要治疗的人数用于展示在每周患者数量的情况下提高成功率所需的干预措施的数量（例如，就诊前电话）。结果共评估就诊217229次，共计480 d，其中22443次（10.33%）符合不合格标准。Hughes等人的模型应用于我们的数据，在预测失败时，接受者工作特征曲线下面积（AUC ROC）为0.678。基于评分的模型的AUC ROC为0.698。Logistic回归、随机森林和梯度增强模型的AUC roc范围为0.7877 ~ 0.7969。如果在低容量周内使用RF分类器选择263名风险最高的患者，则NNT为32，而如果随机选择相同数量的患者，则NNT为90。结论机器学习分类器在预测远程医疗就诊失败方面优于基于分数的方法。需要前瞻性评价；使用NNT作为度量的评估可以帮助这些模型的操作。

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引用次数: 0

LCSNet: Lightweight Caries Segmentation Network for the segmentation of dental caries using smartphone photographs LCSNet：轻量级的龋齿分割网络，用于使用智能手机照片分割龋齿

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-10-09 DOI: 10.1016/j.ibmed.2025.100304

Radha R.C. , B.S. Raghavendra , Rishabh Kumar Hota , K.R. Vijayalakshmi , Seema Patil , A.V. Narasimhadhan

Dental caries is one of the major dental issues that is common among many individuals. It leads to tooth loss and affects the tooth root, creating a need to automatically detect dental caries to reduce treatment costs and prevent its consequences. The Lightweight Caries Segmentation Network (LCSNet) proposed in this study detects the location of dental caries by applying pixel-wise segmentation to dental photographs taken with various Android phones. LCSNet utilizes a Dual Multiscale Residual (DMR) block in both the encoder and decoder, adapts transfer learning through a pre-trained InceptionV3 model at the bottleneck layer, and incorporates a Squeeze and Excitation block in the skip connection, effectively extracting spatial information even from images where 95 % of the background and only 5 % represent the area of interest. A new dataset was developed by gathering oral photographs of dental caries from two hospitals, with advanced augmentation techniques applied. The LCSNet architecture demonstrated an accuracy of 97.36 %, precision of 73.1 %, recall of 70.2 %, an F1-Score of 71.14 %, and an Intersection-over-Union (IoU) of 56.8 %. Expert dentists confirmed that the LCSNet model proposed in this in vivo study accurately segments the position and texture of dental caries. Both qualitative and quantitative performance analyses, along with comparative analyses of efficiency and computational requirements, were conducted with other deep learning models. The proposed model outperforms existing deep learning models and shows significant potential for integration into a smartphone application-based oral disease detection system, potentially replacing some conventional clinically adapted methods.

龋齿是许多人常见的主要牙齿问题之一。它会导致牙齿脱落并影响牙根，因此需要自动检测龋齿，以减少治疗费用并预防其后果。本研究提出的轻量级龋齿分割网络（LCSNet）通过对各种Android手机拍摄的牙齿照片进行逐像素分割来检测龋齿的位置。LCSNet在编码器和解码器中都使用了双多尺度残差（DMR）块，通过瓶颈层预训练的InceptionV3模型适应迁移学习，并在跳过连接中结合了挤压和激励块，即使从95%的背景和只有5%代表感兴趣区域的图像中也能有效地提取空间信息。通过收集来自两家医院的龋齿口腔照片，并应用先进的增强技术，开发了一个新的数据集。LCSNet体系结构的准确率为97.36%，准确率为73.1%，召回率为70.2%，F1-Score为71.14%，IoU为56.8%。专家牙医证实，在体内研究中提出的LCSNet模型准确地分割了蛀牙的位置和质地。定性和定量的性能分析，以及效率和计算需求的比较分析，都与其他深度学习模型进行了比较。所提出的模型优于现有的深度学习模型，并显示出集成到基于智能手机应用程序的口腔疾病检测系统的巨大潜力，有可能取代一些传统的临床适应方法。

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