Healthcare Technology Letters最新文献

{"title":"Ensemble Machine Learning Approaches for Automated Fungal Keratitis Diagnosis Using In Vivo Confocal Microscopy Images","authors":"Sowmya Kamath S.,&nbsp;Shikha Reji,&nbsp;Vaibhava Lakshmi,&nbsp;Supreetha R.,&nbsp;Pratiksha Gawas,&nbsp;Veena Mayya,&nbsp;Manali Hazarika","doi":"10.1049/htl2.70051","DOIUrl":"10.1049/htl2.70051","url":null,"abstract":"<p>Fungal keratitis (FK) is a severe ocular infection that can lead to significant vision problems or blindness if not diagnosed and treated promptly. Early and accurate detection of FK is essential for effective management. Traditional diagnostic methods are often time-consuming and require specialized laboratory resources. Recently, advances in artificial intelligence and computer vision have enabled automated diagnosis of FK using slit-lamp images. In this article, a comprehensive evaluation of state-of-the-art techniques adopted for classifying FK using in vivo confocal microscopy (IVCM) images is presented. Detailed experiments and performance evaluation of various machine learning models are systematically performed, with a focus on evaluating the effect of diverse techniques for image processing, data augmentation, hyperparameters and model finetuning to assess each model's strengths and limitations. Experiments revealed that applying green channel preprocessing with a 12-feature set achieved 99% accuracy using Random Forest, highlighting its effectiveness in FK detection, while complex techniques like histogram modelling reduced accuracy to 64%. Robust models like AdaBoost and RUSBoost maintained high F1-scores, demonstrating adaptability to imbalanced medical datasets, and to real-world clinical scenarios.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12717025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145805804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRISM: Past-Regularized Iterative Self-Distillation With Momentum for Polyp Segmentation","authors":"Tugberk Erol,&nbsp;Tuba Caglikantar,&nbsp;Duygu Sarikaya","doi":"10.1049/htl2.70050","DOIUrl":"10.1049/htl2.70050","url":null,"abstract":"<p>Polyps are abnormal tissue growths in the colon that may develop into colorectal cancer if left undetected. Accurate segmentation in medical imaging is essential for early diagnosis and treatment. Although deep learning has greatly improved polyp segmentation, its dependence on large annotated datasets and substantial computational resources hampers generalization across diverse clinical settings. To overcome these challenges, we propose PRISM, a momentum-based self-distillation method that improves segmentation performance without introducing additional inference cost. Instead of storing or reusing past predictions, PRISM constructs a temporally smoothed teacher model by applying an exponential moving average (EMA) to the model's weights throughout training. This momentum-based teacher provides stable and adaptive supervision signals that co-evolve with the student model. We evaluate PRISM on colonoscopy datasets collected from five distinct medical centres and validate its generalization on an unseen independent dataset. PRISM achieves a Dice score of 0.81 and an IoU of 0.75, outperforming baseline and conventional self-distillation methods. Ablation studies confirm the effectiveness of the EMA-based teacher model in improving segmentation accuracy. PRISM offers a computationally efficient and generalizable solution for polyp segmentation tasks. The code is available at: https://github.com/TugberkErol/PRISM.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12706544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145775856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Markerless Tracking of Robotic Surgical Instruments With Head Mounted Display for Augmented Reality Applications","authors":"Nicholas Greene,&nbsp;Aoqi Long,&nbsp;Yonghao Long,&nbsp;Zheng Han,&nbsp;Qi Dou,&nbsp;Peter Kazanzides","doi":"10.1049/htl2.70044","DOIUrl":"10.1049/htl2.70044","url":null,"abstract":"<p>In robotic-assisted laparoscopic surgery, an assistant surgeon stands at the bedside assisting the intervention, while the surgeon sits at the console teleoperating the robot. Tasks for the assistant include navigating new instruments into the surgeon's field-of-view and passing in or retracting materials from the body using hand-held tools. We previously developed <i>ARssist</i>, an augmented reality application based on an optical see-through head-mounted display (HMD), to aid the assistant. Localization of the HMD with respect to the robot was achieved via the attachment of markers. In this paper, we propose a novel markerless tracking method for robotic instruments using a HoloLens 2 HMD. We first run off-the-shelf YOLOv11 and SAMURAI (an adaptation of Segment Anything 2) networks to detect instrument primitives (shaft lines and keypoints). We then recover full 6D poses via a geometrically interpretable pipeline combining perspective-n-point (PnP) and a multi-view least-squares optimization. We experimentally compare the markerless tracking accuracy to a baseline marker-based tracking solution, and show similar instrument tip accuracy. This suggests that the markerless method is an acceptable substitute to marker-based tracking for this augmented reality application, while avoiding workflow issues with sterilizing and attaching markers.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12706539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145775777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Tool Detection in Laparoscopic Datasets for Surgical Training in Low-Resource Settings","authors":"Omar Choudhry,&nbsp;Sharib Ali,&nbsp;Chandra Shekhar Biyani,&nbsp;Dominic Jones","doi":"10.1049/htl2.70045","DOIUrl":"10.1049/htl2.70045","url":null,"abstract":"&lt;p&gt;In low-resource settings, there is a critical need for skilled surgeons. Alternative training processes that include computer-assisted surgical skill evaluation are essential to address this gap. Using tool detection, surgical videos can be leveraged to derive insights into surgical skill assessment. However, state-of-the-art laparoscopic tool detection methods usually have more complex architectures tailored for in vivo data, which suffer from challenges such as smoke, occlusion, bleeding, etc., which are absent from in vitro training contexts. Thus, this paper tests multiple anchor-based and anchor-free, convolution- and transformer-based, traditional (non-surgical domain-specific) computer vision deep learning state-of-the-art models. With various hardware configurations on a newly curated in-house laparoscopic box-trainer dataset, we emphasise real-time performance on low-cost embedded devices. Overall, the anchor-free YOLOv8-X model was the most accurate, achieving &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;mAP&lt;/mi&gt;\u0000 &lt;mn&gt;50&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm mAP}_{50}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; of 99.5% and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;mAP&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;50&lt;/mn&gt;\u0000 &lt;mo&gt;:&lt;/mo&gt;\u0000 &lt;mn&gt;95&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm mAP}_{50:95}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; of 96.6% with an inference time of 23.5 ms/&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;≈&lt;/mo&gt;\u0000 &lt;annotation&gt;$approx$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;42.6 FPS on an NVIDIA Jetson Orin Nano 8GB (comparable low-cost hardware which could be expected to run real-time skill assessment methods for surgical training boot camps in a resource-constrained environment). The most efficient model was YOLOv11-N, providing 3.1 ms/&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;≈&lt;/mo&gt;\u0000 &lt;annotation&gt;$approx$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;322.6 FPS with a performance difference of +0% &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;mAP&lt;/mi&gt;\u0000 &lt;mn&gt;50&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm mAP}_{50}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and –2.1% &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;mAP&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;50&lt;/mn&gt;\u0000 &lt;mo&gt;:&lt;/mo&gt;\u0000 &lt;mn&gt;95&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm mAP}_{50:95}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. T","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving SLAM-Based Navigation in Flexible Ureteroscopy by Kidney Stone and Surgical Tool Segmentation","authors":"Laura Oliva-Maza,&nbsp;Florian Steidle,&nbsp;Julian Klodmann,&nbsp;Klaus H. Strobl,&nbsp;Arkadiusz Miernik,&nbsp;Rudolph Triebel","doi":"10.1049/htl2.70038","DOIUrl":"10.1049/htl2.70038","url":null,"abstract":"<p>Flexible ureteroscopy is a widely used surgical procedure for diagnosing and treating various urinary tract conditions, particularly kidney stones. Ensuring the complete extraction of all stones is crucial to prevent recurrence and the need for auxiliary interventions. Visual SLAM-based navigation systems have been proposed to assist surgeons by simultaneously estimating the 3D structure of the kidney and tracking the ureteroscope's tip position. However, most existing solutions assume a completely static environment, which does not account for the intraoperative situation. In this study, we extend the work of Oliva Maza et al. by incorporating real-time visual segmentation of kidney stones and surgical tools using either YOLOv7-E6E and segment anything or YOLO11m-seg. Our method discards pixels corresponding to instruments due to their inherent dynamic nature, while kidney stone pixels are incorporated into the SLAM framework but classified as potentially dynamic map points, allowing for their disappearance. This refinement enhances the robustness and the accuracy of ureteroscope position estimation for surgical navigation. To evaluate our approach, we recorded multiple datasets for both segmentation and ureteroscope pose estimation. Experimental results show an average improvement in ureteroscope pose estimation of 35.4% when using YOLOv7-E6E with SAM, and 52.49% when using YOLO11m-seg.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12687663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145726495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalized Few-Shot MM-Former For Surgical Scene Panoptic Segmentation","authors":"Xiaoyan Zhang,&nbsp;Liming Wu,&nbsp;Zhichen Wang,&nbsp;Jingyi Feng,&nbsp;Yichen Zhu,&nbsp;Ziyu Zhou,&nbsp;Ye Tao,&nbsp;Jiquan Liu,&nbsp;Huilong Duan","doi":"10.1049/htl2.70047","DOIUrl":"10.1049/htl2.70047","url":null,"abstract":"<p>Panoptic segmentation is crucial for surgical scene understanding but remains a significant challenge. This is particularly due to the high cost of annotation, which often results in class imbalance in existing datasets, leading to poor performance on categories with limited samples. To address it, we proposed a generalized few-shot MM-former, which is a three-stage framework: (1) We build surgical image-text pairs from the CholecT50 dataset. Using these data, we fine-tune the stable diffusion model to extract multi-scale, image-text fused representations. (2) We train an Mask2Former-based panoptic segmentation decoder on the base classes with sufficient samples, and use it to transform the representations of each image to a set of mask proposals with category predictions. (3) We propose an N-to-M mask matching method. Given a small set of samples from N novel classes, we extract their features as guidance to match M mask proposals, enabling identification of all novel class objects in a single pass. Specifically, each matched proposal is updated with the most likely novel class, while the others keep original predictions. Finally, all proposals are merged to output the results. On CholecPanSeg, our newly built surgical panoptic dataset, the method achieves outstanding results under limited data, surpassing previous approaches.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12686833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145726585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monocular Vision-Based Endoscopic Sinus Navigation: A SLAM Driven Approach With CT Integration","authors":"Roger D. Soberanis-Mukul,&nbsp;Chin Hang Ryan Chan,&nbsp;Ryan Chou,&nbsp;Jan Emily Mangulabnan,&nbsp;Lalithkumar Seenivasan,&nbsp;Xingyu Chen,&nbsp;Mohammad Salehizadeh,&nbsp;S. Swaroop Vedula,&nbsp;Russell H. Taylor,&nbsp;Masaru Ishii,&nbsp;Gregory Hager,&nbsp;Mathias Unberath","doi":"10.1049/htl2.70046","DOIUrl":"10.1049/htl2.70046","url":null,"abstract":"<p>Surgical navigation is critical in sinus surgery to enhance the surgeon's spatial awareness and improve precision, particularly around occluded critical structures. While external tracker-based navigation systems exist, vision-based solutions are preferred for being less intrusive and for enabling endoscopic image analysis to assist surgeons. However, monocular endoscopy navigation faces challenges associated with monocular reconstruction and camera pose estimation. This paper presents a proof of concept for monocular vision-based sinus navigation that utilizes only preoperative CT data and the endoscope video stream to navigate the sinus anatomy. We developed a vision-based navigation system that incorporates a SLAM algorithm to estimate the camera pose and reconstruct the 3D surface of the anatomy. Given an initial semi-automated registration, the algorithm maps the SLAM-based trajectories to the CT space while employing the reconstructed point cloud to solve for the scale interactively. The system displays the updates in the CT triplane visualization as SLAM reconstructs the scene and recovers pose information. We tested our system by performing an off-site navigation in ten recorded endoscopic video streaming generated from sequences obtained from eight cadaveric subjects, comparing the vision-based navigation to reference optical tracker pose data and obtaining translation and rotation errors of 3.2 mm and 4.9 degrees, respectively. Additionally, we performed three on-site tests of our system on two different cadaver experiments. Our work evaluates a fully integrated system that closes the loop between image-based reconstruction and CT visualization, and discusses the challenges to address to achieve clinical level surgical navigation.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12686831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145726535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Learning-Based Reconstructed Liver Surfaces From Partial Point Clouds for Improving Pre- to Intra-Operative 3D to 3D Registration","authors":"Nakul Poudel,&nbsp;Zixin Yang,&nbsp;Richard Simon,&nbsp;Cristian A. Linte","doi":"10.1049/htl2.70041","DOIUrl":"10.1049/htl2.70041","url":null,"abstract":"<p>The registration process for fusing pre-operative information with intra-operative data collected during image-guided liver surgery struggles due to partial visibility. Learning-based partial point cloud-to-complete surface generation has shown a promising direction for improving registration outcomes. Yet, the intra-operative liver surface can undergo significant deformation, leading to geometric discrepancies from its pre-operative shape and introducing error in the completed intra-operative surface. It is essential to understand the error introduced during surface generation and its impact on both rigid and non-rigid registration to ensure robust performance in clinical settings. In this study, we leveraged a VN-OccNet framework trained in a patient-specific manner on simulated deformed data to generate complete surfaces from partial observations extracted from five viewpoints across four in vitro liver phantoms. We first analysed the error associated with the generated complete surface mesh from the partial point cloud, then integrated the complete surface generated into Go-ICP and GMM-FEM registration. Furthermore, we estimated the registration error separately for visible and invisible regions. Our results indicate that the error in the generated surface is more significant further away from the partially visible liver surface, and it could affect the registration, not only in the invisible region, but to some extent also in the visible region within the camera's field of view.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12686830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145726559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EgoSurgery-HTS: A Dataset for Egocentric Hand–Tool Segmentation in Open Surgery Videos","authors":"Nathan Darjana,&nbsp;Ryo Fujii,&nbsp;Hideo Saito,&nbsp;Hiroki Kajita","doi":"10.1049/htl2.70049","DOIUrl":"10.1049/htl2.70049","url":null,"abstract":"<p>Egocentric open-surgery videos capture rich, fine-grained details essential for accurately modelling surgical procedures and human behaviour in the operating room. A detailed, pixel-level understanding of hands and surgical tools is crucial for interpreting a surgeon action and intention. We introduce EgoSurgery-HTS, a new dataset with pixel-wise annotations and a benchmark suite for segmenting surgical tools, hands, and interacting tools in egocentric open-surgery videos. Specifically, we provide a labelled dataset for (1) tool instance segmentation of 14 distinct surgical tools, (2) hand instance segmentation, and (3) hand–tool segmentation to label main operating hands and the tools they manipulate. Using EgoSurgery-HTS, we conduct extensive evaluations of state-of-the-art segmentation methods and demonstrate significant improvements in the accuracy of hand and hand–tool segmentation in egocentric open-surgery videos compared to existing datasets. The dataset will be released upon acceptance.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12686825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145726573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148071463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}