Robot-assisted augmented reality surgical navigation based on optical tracking for mandibular reconstruction surgery.

PURPOSE: This work proposes a robot-assisted augmented reality (AR) surgical navigation system for mandibular reconstruction. The system accurately superimposes the preoperative osteotomy plan of the mandible and fibula into a real scene. It assists the doctor in osteotomy quickly and safely under the guidance of the robotic arm. METHODS: The proposed system mainly consists of two modules: the AR guidance module of the mandible and fibula and the robot navigation module. In the AR guidance module, we propose an AR calibration method based on the spatial registration of the image tracking marker to superimpose the virtual models of the mandible and fibula into the real scene. In the robot navigation module, the posture of the robotic arm is first calibrated under the tracking of the optical tracking system. The robotic arm can then be positioned at the planned osteotomy after the registration of the computed tomography image and the patient position. The combined guidance of AR and robotic arm can enhance the safety and precision of the surgery. RESULTS: The effectiveness of the proposed system was quantitatively assessed on cadavers. In the AR guidance module, osteotomies of the mandible and fibula achieved mean errors of 1.61 ± 0.62 and 1.08 ± 0.28 mm, respectively. The mean reconstruction error of the mandible was 1.36 ± 0.22 mm. In the AR-robot guidance module, the mean osteotomy errors of the mandible and fibula were 1.47 ± 0.46 and 0.98 ± 0.24 mm, respectively. The mean reconstruction error of the mandible was 1.20 ± 0.36 mm. CONCLUSIONS: The cadaveric experiments of 12 fibulas and six mandibles demonstrate the proposed system's effectiveness and potential clinical value in reconstructing the mandibular defect with a free fibular flap.

3D ARCNN: An Asymmetric Residual CNN for False Positive Reduction in Pulmonary Nodule.

Lung cancer is with the highest morbidity and mortality, and detecting cancerous lesions early is essential for reducing mortality rates. Deep learning-based lung nodule detection techniques have shown better scalability than traditional methods. However, pulmonary nodule test results often include a number of false positive outcomes. In this paper, we present a novel asymmetric residual network called 3D ARCNN that leverages 3D features and spatial information of lung nodules to improve classification performance. The proposed framework uses an internally cascaded multi-level residual model for fine-grained learning of lung nodule features and multi-layer asymmetric convolution to address the problem of large neural network parameters and poor reproducibility. We evaluate the proposed framework on the LUNA16 dataset and achieve a high detection sensitivity of 91.6%, 92.7%, 93.2%, and 95.8% for 1, 2, 4, and 8 false positives per scan, respectively, with an average CPM index of 0.912. Quantitative and qualitative evaluations demonstrate the superior performance of our framework compared to existing methods. 3D ARCNN framework can effectively reduce the possibility of false positive lung nodules in the clinical.

Transplantation of Wnt3a-modified neural stem cells promotes neural regeneration and functional recovery after spinal cord injury via Wnt-Gli2 pathway.

Neural stem cell (NSCs) transplantation has great potential in the treatment of spinal cord injury (SCI). Previous studies have indicated that the Wnt pathway could regulate the expression of basic helix-loop-helix (bHLH) family factor Hes5 and Mash1 in NSCs, but not through the notch intracellular domain. This suggests that there are other signals involved in this process. The aim of this study was to investigate the role of Wnt-Gli2 pathway in the treatment of SCI by transplanting neural stem cells. NSCs were isolated from the striata of embryonic day 14 mice. Activation of the Wnt pathway was achieved using Wnt3a protein, while Gli2 was inhibited using Gli2-siRNA. Expression levels of Gli2 and bHLH factors were assessed using western blotting. NSCs proliferation was evaluated using CCK-8 assay, and neural differentiation was determined by immunofluorescence staining. Finally, the modified NSCs were transplanted into mice with SCI, and their effects were assessed using behavioral and histological tests. Our results demonstrated that Wnt3a promoted the expression of Mash1 through Gli2. Moreover, the expression of Ngn1 and Hes1 was up-regulated, while Hes5 was down-regulated. Wnt3a also promoted NSCs proliferation and neural differentiation through this signaling pathway. In vivo experiments showed that NSCs transplantation mediated by Wnt3a-Gli2 signaling increased the number of neurons and resulted in improved Basso Mouse Scale scores. In conclusion, our findings suggest that Gli2 plays a role in mediating the regulation of Wnt3a signaling on promoting NSCs proliferation and neural differentiation. This pathway is therefore important in NSCs-mediated SCI recovery.

Building a trustworthy AI differential diagnosis application for Crohn's disease and intestinal tuberculosis.

BACKGROUND: Differentiating between Crohn's disease (CD) and intestinal tuberculosis (ITB) with endoscopy is challenging. We aim to perform more accurate endoscopic diagnosis between CD and ITB by building a trustworthy AI differential diagnosis application. METHODS: A total of 1271 electronic health record (EHR) patients who had undergone colonoscopies at Peking Union Medical College Hospital (PUMCH) and were clinically diagnosed with CD (n = 875) or ITB (n = 396) were used in this study. We build a workflow to make diagnoses with EHRs and mine differential diagnosis features; this involves finetuning the pretrained language models, distilling them into a light and efficient TextCNN model, interpreting the neural network and selecting differential attribution features, and then adopting manual feature checking and carrying out debias training. RESULTS: The accuracy of debiased TextCNN on differential diagnosis between CD and ITB is 0.83 (CR F1: 0.87, ITB F1: 0.77), which is the best among the baselines. On the noisy validation set, its accuracy was 0.70 (CR F1: 0.87, ITB: 0.69), which was significantly higher than that of models without debias. We also find that the debiased model more easily mines the diagnostically significant features. The debiased TextCNN unearthed 39 diagnostic features in the form of phrases, 17 of which were key diagnostic features recognized by the guidelines. CONCLUSION: We build a trustworthy AI differential diagnosis application for differentiating between CD and ITB focusing on accuracy, interpretability and robustness. The classifiers perform well, and the features which had statistical significance were in agreement with clinical guidelines.

CT2X-IRA: CT to x-ray image registration agent using domain-cross multi-scale-stride deep reinforcement learning.

Objective.In computer-assisted minimally invasive surgery, the intraoperative x-ray image is enhanced by overlapping it with a preoperative CT volume to improve visualization of vital anatomical structures. Therefore, accurate and robust 3D/2D registration of CT volume and x-ray image is highly desired in clinical practices. However, previous registration methods were prone to initial misalignments and struggled with local minima, leading to issues of low accuracy and vulnerability.Approach.To improve registration performance, we propose a novel CT/x-ray image registration agent (CT2X-IRA) within a task-driven deep reinforcement learning framework, which contains three key strategies: (1) a multi-scale-stride learning mechanism provides multi-scale feature representation and flexible action step size, establishing fast and globally optimal convergence of the registration task. (2) A domain adaptation module reduces the domain gap between the x-ray image and digitally reconstructed radiograph projected from the CT volume, decreasing the sensitivity and uncertainty of the similarity measurement. (3) A weighted reward function facilitates CT2X-IRA in searching for the optimal transformation parameters, improving the estimation accuracy of out-of-plane transformation parameters under large initial misalignments.Main results.We evaluate the proposed CT2X-IRA on both the public and private clinical datasets, achieving target registration errors of 2.13 mm and 2.33 mm with the computation time of 1.5 s and 1.1 s, respectively, showing an accurate and fast workflow for CT/x-ray image rigid registration.Significance.The proposed CT2X-IRA obtains the accurate and robust 3D/2D registration of CT and x-ray images, suggesting its potential significance in clinical applications.

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

M-CSAFN: Multi-Color Space Adaptive Fusion Network for Automated Port-Wine Stains Segmentation.

Automatic segmentation of port-wine stains (PWS) from clinical images is critical for accurate diagnosis and objective assessment of PWS. However, this is a challenging task due to the color heterogeneity, low contrast, and indistinguishable appearance of PWS lesions. To address such challenges, we propose a novel multi-color space adaptive fusion network (M-CSAFN) for PWS segmentation. First, a multi-branch detection model is constructed based on six typical color spaces, which utilizes rich color texture information to highlight the difference between lesions and surrounding tissues. Second, an adaptive fusion strategy is used to fuse complementary predictions, which address the significant differences within the lesions caused by color heterogeneity. Third, a structural similarity loss with color information is proposed to measure the detail error between predicted lesions and truth lesions. Additionally, a PWS clinical dataset consisting of 1413 image pairs was established for the development and evaluation of PWS segmentation algorithms. To verify the effectiveness and superiority of the proposed method, we compared it with other state-of-the-art methods on our collected dataset and four publicly available skin lesion datasets (ISIC 2016, ISIC 2017, ISIC 2018, and PH2). The experimental results show that our method achieves remarkable performance in comparison with other state-of-the-art methods on our collected dataset, achieving 92.29% and 86.14% on Dice and Jaccard metrics, respectively. Comparative experiments on other datasets also confirmed the reliability and potential capability of M-CSAFN in skin lesion segmentation.

Current status of indoor dust PBDE pollution and its physical burden and health effects on children.

Polybrominated diphenyl ethers (PBDEs) are widely detected in indoor dust, which has been identified as a more important route of PBDE exposure for children than food intake. The physical burden and health hazards to children of PBDE exposure in house dust have not been adequately summarized; therefore, this article reviews the current status of PBDE pollution in indoor dust associated with children, highlighting the epidemiological evidence for physical burden and health risks in children. We find that PBDEs remain at high levels in indoor dust, including in homes, schools, and cars, especially in cars showing a significant upward trend. There is a trend towards an increase in the proportion of BDE-209 in household dust, which is indicative of recent PBDE contamination. Conversely, PBDE congeners in car and school indoor dust tended to shift from highly brominated to low brominated, suggesting a shift in current pollution patterns. Indoor dust exposure causes significantly higher PBDE burdens in children, especially infants in early life, than in adults. Exposure to dust also affects breast milk, putting infants at high risk of exposure. Although evidence is limited, available epidemiological studies suggest that exposure to indoor dust PBDEs promotes neurobehavioral problems and cancer development in children.

Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer.

Background: Medical imaging is critical in clinical practice, and high value radiological reports can positively assist clinicians. However, there is a lack of methods for determining the value of reports. Objective: The purpose of this study was to establish an ensemble learning classification model using natural language processing (NLP) applied to the Chinese free text of radiological reports to determine their value for liver lesion detection in patients with colorectal cancer (CRC). Methods: Radiological reports of upper abdominal computed tomography (CT) and magnetic resonance imaging (MRI) were divided into five categories according to the results of liver lesion detection in patients with CRC. The NLP methods including word segmentation, stop word removal, and n-gram language model establishment were applied for each dataset. Then, a word-bag model was built, high-frequency words were selected as features, and an ensemble learning classification model was constructed. Several machine learning methods were applied, including logistic regression (LR), random forest (RF), and so on. We compared the accuracy between priori choosing pertinent word strings and our machine language methodologies. Results: The dataset of 2790 patients included CT without contrast (10.2%), CT with/without contrast (73.3%), MRI without contrast (1.8%), and MRI with/without contrast (14.6%). The ensemble learning classification model determined the value of reports effectively, reaching 95.91% in the CT with/without contrast dataset using XGBoost. The logistic regression, random forest, and support vector machine also achieved good classification accuracy, reaching 95.89%, 95.04%, and 95.00% respectively. The results of XGBoost were visualized using a confusion matrix. The numbers of errors in categories I, II and V were very small. ELI5 was used to select important words for each category. Words such as "no abnormality", "suggest", "fatty liver", and "transfer" showed a relatively large degree of positive correlation with classification accuracy. The accuracy based on string pattern search method model was lower than that of machine learning. Conclusions: The learning classification model based on NLP was an effective tool for determining the value of radiological reports focused on liver lesions. The study made it possible to analyze the value of medical imaging examinations on a large scale.

Multi3:multi-templates siamese network withmulti-peaks detection andmulti-features refinement for target tracking in ultrasound image sequences.

Objective. Radiation therapy requires a precise target location. However, respiratory motion increases the uncertainties of the target location. Accurate and robust tracking is significant for improving operation accuracy.Approach. In this work, we propose a tracking framework Multi3, including a multi-templates Siamese network, multi-peaks detection and multi-features refinement, for target tracking in ultrasound sequences. Specifically, we use two templates to provide the location and deformation of the target for robust tracking. Multi-peaks detection is applied to extend the set of potential target locations, and multi-features refinement is designed to select an appropriate location as the tracking result by quality assessment.Main results. The proposed Multi3 is evaluated on a public dataset, i.e. MICCAI 2015 challenge on liver ultrasound tracking (CLUST), and our clinical dataset provided by the Chinese People's Liberation Army General Hospital. Experimental results show that Multi3 achieves accurate and robust tracking in ultrasound sequences (0.75 ± 0.62 mm and 0.51 ± 0.32 mm tracking errors in two datasets).Significance. The proposed Multi3 is the most robust method on the CLUST 2D benchmark set, exhibiting potential in clinical practice.

Densely Connected U-Net with Criss-Cross Attention for Automatic Liver Tumor Segmentation in CT Images.

Automatic liver tumor segmentation plays a key role in radiation therapy of hepatocellular carcinoma. In this paper, we propose a novel densely connected U-Net model with criss-cross attention (CC-DenseUNet) to segment liver tumors in computed tomography (CT) images. The dense interconnections in CC-DenseUNet ensure the maximum information flow between encoder layers when extracting intra-slice features of liver tumors. Moreover, the criss-cross attention is used in CC-DenseUNet to efficiently capture only the necessary and meaningful non-local contextual information of CT images containing liver tumors. We evaluated the proposed CC-DenseUNet on the LiTS dataset and the 3DIRCADb dataset. Experimental results show that the proposed method reaches the state-of-the-art performance for liver tumor segmentation. We further experimentally demonstrate the robustness of the proposed method on a clinical dataset comprising 20 CT volumes.

Joint estimation of depth and motion from a monocular endoscopy image sequence using a multi-loss rebalancing network.

Building an in vivo three-dimensional (3D) surface model from a monocular endoscopy is an effective technology to improve the intuitiveness and precision of clinical laparoscopic surgery. This paper proposes a multi-loss rebalancing-based method for joint estimation of depth and motion from a monocular endoscopy image sequence. The feature descriptors are used to provide monitoring signals for the depth estimation network and motion estimation network. The epipolar constraints of the sequence frame is considered in the neighborhood spatial information by depth estimation network to enhance the accuracy of depth estimation. The reprojection information of depth estimation is used to reconstruct the camera motion by motion estimation network with a multi-view relative pose fusion mechanism. The relative response loss, feature consistency loss, and epipolar consistency loss function are defined to improve the robustness and accuracy of the proposed unsupervised learning-based method. Evaluations are implemented on public datasets. The error of motion estimation in three scenes decreased by 42.1%,53.6%, and 50.2%, respectively. And the average error of 3D reconstruction is 6.456 ± 1.798mm. This demonstrates its capability to generate reliable depth estimation and trajectory reconstruction results for endoscopy images and meaningful applications in clinical.

Augmented reality calibration using feature triangulation iteration-based registration for surgical navigation.

BACKGROUND: Marker-based augmented reality (AR) calibration methods for surgical navigation often require a second computed tomography scan of the patient, and their clinical application is limited due to high manufacturing costs and low accuracy. METHODS: This work introduces a novel type of AR calibration framework that combines a Microsoft HoloLens device with a single camera registration module for surgical navigation. A camera is used to gather multi-view images of a patient for reconstruction in this framework. A shape feature matching-based search method is proposed to adjust the size of the reconstructed model. The double clustering-based 3D point cloud segmentation method and 3D line segment detection method are also proposed to extract the corner points of the image marker. The corner points are the registration data of the image marker. A feature triangulation iteration-based registration method is proposed to quickly and accurately calibrate the pose relationship between the image marker and the patient in the virtual and real space. The patient model after registration is wirelessly transmitted to the HoloLens device to display the AR scene. RESULTS: The proposed approach was used to conduct accuracy verification experiments on the phantoms and volunteers, which were compared with six advanced AR calibration methods. The proposed method obtained average fusion errors of 0.70 ± 0.16 and 0.91 ± 0.13 mm in phantom and volunteer experiments, respectively. The fusion accuracy of the proposed method is the highest among all comparison methods. A volunteer liver puncture clinical simulation experiment was also conducted to show the clinical feasibility. CONCLUSIONS: Our experiments proved the effectiveness of the proposed AR calibration method, and revealed a considerable potential for improving surgical performance.

Endoscopy image enhancement method by generalized imaging defect models based adversarial training.

Objective.Smoke, uneven lighting, and color deviation are common issues in endoscopic surgery, which have increased the risk of surgery and even lead to failure.Approach.In this study, we present a new physics model driven semi-supervised learning framework for high-quality pixel-wise endoscopic image enhancement, which is generalizable for smoke removal, light adjustment, and color correction. To improve the authenticity of the generated images, and thereby improve the network performance, we integrated specific physical imaging defect models with the CycleGAN framework. No ground-truth data in pairs are required. In addition, we propose a transfer learning framework to address the data scarcity in several endoscope enhancement tasks and improve the network performance.Main results.Qualitative and quantitative studies reveal that the proposed network outperforms the state-of-the-art image enhancement methods. In particular, the proposed method performs much better than the original CycleGAN, for example, the structural similarity improved from 0.7925 to 0.8648, feature similarity for color images from 0.8917 to 0.9283, and quaternion structural similarity from 0.8097 to 0.8800 in the smoke removal task. Experimental results of the proposed transfer learning method also reveal its superior performance when trained with small datasets of target tasks.Significance.Experimental results on endoscopic images prove the effectiveness of the proposed network in smoke removal, light adjustment, and color correction, showing excellent clinical usefulness.

Fusion Siamese network with drift correction for target tracking in ultrasound sequences.

Motion tracking techniques can revise the bias arising from respiration-caused motion in radiation therapy. Tracking key structures accurately and at a real-time speed is necessary for effective motion tracking. In this work, we propose a fusion Siamese network with drift correction for target tracking in ultrasound sequences. Specifically, the network fuses four response maps generated by the cross-correlation between convolution layers at different resolutions to reduce up-sampling error. A correction strategy combining local structural similarity and target trajectory is proposed to revise the target drift predicted by the network. Moreover, a coarse-to-fine strategy is proposed to train the network with a limited number of annotated images, in which an augmented dataset is generated by corner points to learn network features with high generalizability. The proposed method is evaluated on the basis of the public dataset of the MICCAI 2015 Challenge on Liver UltraSound Tracking (CLUST) and our ultrasound image dataset, which is provided by the Chinese People's Liberation Army General Hospital (CPLAGH). A tracking error of 0.80 ± 1.16 mm is observed for 85 targets across 39 ultrasound sequences in the CLUST dataset. A tracking error of 0.61 ± 0.36 mm is observed for 20 targets across 10 ultrasound sequences in the CPLAGH dataset. The effectiveness of the proposed fusion and correction strategies is verified via two ablation experiments. Overall, the experimental results demonstrate the effectiveness of the proposed fusion Siamese network with drift correction and reveal its potential in clinical practice.

An optimal ablation time prediction model based on minimizing the relapse risk.

OBJECTIVE: Percutaneous microwave ablation is an essential and safe method for the treatment of liver cancer. As one therapeutic dose, ablation time is crucial to the treatment effect determined by the physicians. However, due to the different experiences of physicians and the significant individual differences of patients, the final treatment effect is also different, which makes it difficult for the ablation time recorded in the electronic health records (EHRs) to follow the same pattern. To solve this problem, we propose a data mining method based on historical treatment data recorded in EHR, which uses a robust relapse risk as strong supervision to correct the ablation time. The prediction results of this method are closer to the situation of patients without relapse, which can provide physicians with reference. METHODS: In the proposed method, we introduce the optimization method to iteratively minimize the postoperative relapse risk and utilize gradient propagation between the risk and ablation time during iteration to correct the latter. We also apply a self-attention mechanism to find the global dependencies between each feature in EHR to improve the final prediction performance of the model. RESULTS: Comparative experimental results show that compared with other baseline model, the proposed model achieves better performance on R-square, MAE, and MSE metric. The results of ablation experiments show that the integration of label correction and self-attention mechanism can improve the model performance. CONCLUSIONS: We using relapse risk as strong supervision related to the ablation time can effectively correct the deviation of the ablation time as weak supervision. The self-attention mechanism in the proposed model can significantly improve the prediction performance.

Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches.

BACKGROUND: Differentiating between ulcerative colitis (UC), Crohn's disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases through machine learning algorithms. METHODS: A total of 6399 consecutive patients (5128 UC, 875 CD and 396 ITB) who had undergone colonoscopy examinations in the Peking Union Medical College Hospital from January 2008 to November 2018 were enrolled. The input was the description of the endoscopic image in the form of free text. Word segmentation and key word filtering were conducted as data preprocessing. Random forest (RF) and convolutional neural network (CNN) approaches were applied to different disease entities. Three two-class classifiers (UC and CD, UC and ITB, and CD and ITB) and a three-class classifier (UC, CD and ITB) were built. RESULTS: The classifiers built in this research performed well, and the CNN had better performance in general. The RF sensitivities/specificities of UC-CD, UC-ITB, and CD-ITB were 0.89/0.84, 0.83/0.82, and 0.72/0.77, respectively, while the values for the CNN of CD-ITB were 0.90/0.77. The precisions/recalls of UC-CD-ITB when employing RF were 0.97/0.97, 0.65/0.53, and 0.68/0.76, respectively, and when employing the CNN were 0.99/0.97, 0.87/0.83, and 0.52/0.81, respectively. CONCLUSIONS: Classifiers built by RF and CNN approaches had excellent performance when classifying UC with CD or ITB. For the differentiation of CD and ITB, high specificity and sensitivity were achieved as well. Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases. CONFERENCE: The abstract of this article has won the first prize of the Young Investigator Award during the Asian Pacific Digestive Week (APDW) 2019 held in Kolkata, India.

Blood concentrations of lead, cadmium, mercury and their association with biomarkers of DNA oxidative damage in preschool children living in an e-waste recycling area.

Reactive oxygen species (ROS)-induced DNA damage occurs in heavy metal exposure, but the simultaneous effect on DNA repair is unknown. We investigated the influence of co-exposure of lead (Pb), cadmium (Cd), and mercury (Hg) on 8-hydroxydeoxyguanosine (8-OHdG) and human repair enzyme 8-oxoguanine DNA glycosylase (hOGG1) mRNA levels in exposed children to evaluate the imbalance of DNA damage and repair. Children within the age range of 3-6 years from a primitive electronic waste (e-waste) recycling town were chosen as participants to represent a heavy metal-exposed population. 8-OHdG in the children's urine was assessed for heavy metal-induced oxidative effects, and the hOGG1 mRNA level in their blood represented the DNA repair ability of the children. Among the children surveyed, 88.14% (104/118) had a blood Pb level >5 µg/dL, 22.03% (26/118) had a blood Cd level >1 µg/dL, and 62.11% (59/95) had a blood Hg level >10 µg/dL. Having an e-waste workshop near the house was a risk factor contributing to high blood Pb (r s  = 0.273, p < 0.01), while Cd and Hg exposure could have come from other contaminant sources. Preschool children of fathers who had a college or university education had significantly lower 8-OHdG levels (median 242.76 ng/g creatinine, range 154.62-407.79 ng/g creatinine) than did children of fathers who had less education (p = 0.035). However, we did not observe a significant difference in the mRNA expression levels of hOGG1 between the different variables. Compared with children having low lead exposure (quartile 1), the children with high Pb exposure (quartiles 2, 3, and 4) had significantly higher 8-OHdG levels (ß Q2 = 0.362, 95% CI 0.111-0.542; ß Q3 = 0.347, 95% CI 0.103-0.531; ß Q4 = 0.314, 95% CI 0.087-0.557). Associations between blood Hg levels and 8-OHdG were less apparent. Compared with low levels of blood Hg (quartile 1), elevated blood Hg levels (quartile 2) were associated with higher 8-OHdG levels (ß Q2 = 0.236, 95% CI 0.039-0.406). Compared with children having low lead exposure (quartile 1), the children with high Pb exposure (quartiles 2, 3, and 4) had significantly higher 8-OHdG levels.