Asia-Pacific Journal of Ophthalmology最新文献

{"title":"Practice Patterns and Challenges in Managing Inherited Retinal Diseases Across Asia-Pacific: A Survey from the APIED Network","authors":"Wendy M. Wong ,&nbsp;Yih Chung Tham ,&nbsp;Lauren N. Ayton ,&nbsp;Alexis Ceecee Britten-Jones ,&nbsp;Thomas L. Edwards ,&nbsp;John Grigg ,&nbsp;Matthew P. Simunovic ,&nbsp;Fred K. Chen ,&nbsp;Zi-Bing Jin ,&nbsp;Ren-Juan Shen ,&nbsp;Ruifang Sui ,&nbsp;Liping Yang ,&nbsp;Chen Zhao ,&nbsp;Haoyu Chen ,&nbsp;Shiying Li ,&nbsp;Xiaoyan Ding ,&nbsp;Muna Bhende ,&nbsp;Rajiv Raman ,&nbsp;Parveen Sen ,&nbsp;B. Poornachandra ,&nbsp;Hwei Wuen Chan","doi":"10.1016/j.apjo.2024.100098","DOIUrl":"10.1016/j.apjo.2024.100098","url":null,"abstract":"<div><h3>Purpose</h3><div>The objective of this paper is to shed light on the current landscape of genotyping practices, phenotyping practices and availability of essential vision rehabilitation management for inherited retinal diseases (IRD) in the Asia-Pacific (APAC) Region.</div></div><div><h3>Methods</h3><div>The 62-item questionnaire was distributed electronically via email. The questions covered five domains: (1) structure of the IRD service and registry/database; (2) genotyping practices; (3) genetic counselling; (4) deep phenotyping practices; (5) low-vision rehabilitation services.</div></div><div><h3>Results</h3><div>The survey was completed by 36 of 45 centres in twelve countries and regions in APAC. Among these centres, 42 % reported managing more than 1000 patients. Notably, 39 % of centres lack an IRD database or registry, and 44 % of centres have tested less than one-quarter of their IRD patients. The majority of centres (67 %) do not have genetic counsellors. While there was consistency in the imaging-based investigations, there was marked heterogeneity for functional testing using electrophysiology and formal perimetry. Only 34 % of centres confirmed the availability of access to low-vision assistive devices.</div></div><div><h3>Conclusions</h3><div>This study reveals several critical gaps in managing IRDs in the APAC region. These include the lack of IRD database/registry in one-third of centres, a substantial proportion of patients remaining genetically undiagnosed, and limited availability of genetic counsellors. The findings also underscore a need to harmonise investigations for evaluating retinal function and identify areas for improvement in the provision of low-vision rehabilitation services.</div></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 5","pages":"Article 100098"},"PeriodicalIF":3.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142375001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of childhood atropine treatment on adult choroidal thickness using sequential deep learning-enabled segmentation","authors":"Yong Li ,&nbsp;Damon Wong ,&nbsp;Syna Sreng ,&nbsp;Joey Chung ,&nbsp;Angeline Toh ,&nbsp;Han Yuan ,&nbsp;Leila Sara Eppenberger ,&nbsp;Cheryl Leow ,&nbsp;Daniel Ting ,&nbsp;Nan Liu ,&nbsp;Leopold Schmetterer ,&nbsp;Seang-Mei Saw ,&nbsp;Jost B. Jonas ,&nbsp;Audrey Chia ,&nbsp;Marcus Ang","doi":"10.1016/j.apjo.2024.100107","DOIUrl":"10.1016/j.apjo.2024.100107","url":null,"abstract":"<div><h3>Purpose</h3><div>To describe choroidal thickness measurements using a sequential deep learning segmentation in adults who received childhood atropine treatment for myopia control.</div></div><div><h3>Design</h3><div>Prospective, observational study.</div></div><div><h3>Methods</h3><div>Choroidal thickness was measured by swept-source optical coherence tomography in adults who received childhood atropine, segmented using a sequential deep learning approach.</div></div><div><h3>Results</h3><div>Of 422 eyes, 94 (22.3 %) had no previous exposure to atropine treatment, while 328 (77.7 %) had received topical atropine during childhood. After adjusting for age, sex, and axial length, childhood atropine exposure was associated with a thicker choroid by 32.1 μm (95 % CI, 9.2–55.0; <em>P</em> = 0.006) in the inner inferior, 23.5 μm (95 % CI, 1.9–45.1; <em>P</em> = 0.03) in the outer inferior, 21.8 μm (95 % CI, 0.76–42.9; <em>P</em> = 0.04) in the inner nasal, and 21.8 μm (95 % CI, 2.6–41.0; <em>P</em> = 0.03) in the outer nasal. Multivariable analysis, adjusted for age, sex, atropine use, and axial length, showed an independent association between central subfield choroidal thickness and the incidence of tessellated fundus (<em>P</em> &lt; 0.001; OR, 0.97; 95 % CI, 0.96–0.98).</div></div><div><h3>Conclusions</h3><div>This study demonstrated that short-term (2–4 years) atropine treatment during childhood was associated with an increase in choroidal thickness of 20–40 μm in adulthood (10–20 years later), after adjusting for age, sex, and axial length. We also observed an independent association between eyes with thicker central choroidal measurements and reduced incidence of tessellated fundus. Our study suggests that childhood exposure to atropine treatment may affect choroidal thickness in adulthood.</div></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 5","pages":"Article 100107"},"PeriodicalIF":3.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gain of chromosome 8q and high expression of EZH2 may predict poor prognosis in Chinese patients with uveal melanoma","authors":"Lingyu Zhang ,&nbsp;Hui Pan ,&nbsp;Yiran Yao ,&nbsp;Xiang Gu ,&nbsp;Tongxin Ge ,&nbsp;Junqi Cui ,&nbsp;Peiwei Chai ,&nbsp;Xiaofang Xu ,&nbsp;Renbing Jia ,&nbsp;Ai Zhuang ,&nbsp;Xianqun Fan","doi":"10.1016/j.apjo.2024.100108","DOIUrl":"10.1016/j.apjo.2024.100108","url":null,"abstract":"<div><h3>Purpose</h3><div>To explore risk factors predicting poor prognosis of uveal melanoma in a Chinese population, with specific emphasis on monosomy 3, 8q gain, and EZH2 staining.</div></div><div><h3>Methods</h3><div>Eighty-nine patients with uveal melanoma from 2012 to 2021 were reviewed. Clinical and pathological records were collected and analyzed. Immunohistochemical staining of EZH2, monosomy 3 and 8q gain were respectively conducted in 45, 54, and 57 cases. Survival was evaluated by Kaplan–Meier analysis and log-rank test. Cox proportional hazard regressions were employed to predict risk factors of distant metastasis.</div></div><div><h3>Results</h3><div>The median follow-up was 44 months. Altogether, 16 % of patients developed distant metastases and died from disease-related causes. Disease-specific survival at one and three years was 96.6 % and 88.4 % while distant metastasis rates were 7.9 % and 12 %. Univariate Cox regression analysis revealed that age (HR: 1.04), tumor largest basal diameter (HR: 1.21), tumor thickness (HR: 1.21), ciliary body involvement (HR: 3.50), AJCC stage (HR: 5.68), epithelioid cell type (HR: 7.71), 8q gain (HR: 7.48), and high expression of EZH2 (HR: 6.09) were associated with distant metastasis. 8q gain was associated with epithelioid cell type and thicker tumor while EZH2 was correlated with epithelioid cell type. Monosomy 3 lacked a significant correlation with other factors.</div></div><div><h3>Conclusion</h3><div>EZH2 and 8q gain could be taken into consideration when calculating poor prognosis in Chinese patients with uveal melanoma. Monosomy 3 showed no significance in distant metastasis, but this may be due to a small sample size.</div></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 5","pages":"Article 100108"},"PeriodicalIF":3.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142456904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Testing of Artificial Intelligence-Based Mobile Application to Achieve Cataract Backlog-Free Status in Uttar Pradesh, India","authors":"Madhavi Devaraj ,&nbsp;Vasanthakumar Namasivayam ,&nbsp;Satya Swarup Srichandan ,&nbsp;Eshan Sharma ,&nbsp;Apjit Kaur ,&nbsp;Nibha Mishra ,&nbsp;Dev Vimal Seth ,&nbsp;Akanksha Singh ,&nbsp;Pankaj Saxena ,&nbsp;Eshaan Vasanthakumar ,&nbsp;James Blanchard ,&nbsp;Ravi Prakash","doi":"10.1016/j.apjo.2024.100094","DOIUrl":"10.1016/j.apjo.2024.100094","url":null,"abstract":"<div><h3>Background</h3><div>Uttar Pradesh (UP), the most populous state in India, has about 36 million people aged 50 years or older, spread across more than 100,000 villages. Among them, an estimated 3.5 million suffer from visual impairments, including blindness due to untreated cataracts. To achieve cataract backlog-free status, UP is required to screen this population at the community level and provide treatment to those suffering from cataracts. We envisioned an AI-powered primary screening app utilizing eye images, deployable to frontline health workers for community-level screening. This paper outlines insights gained from developing the AI mobile app “Roshni” for cataract screening.</div></div><div><h3>Method</h3><div>The AI-based cataract classification model was developed using 13,633 eye images and finalized after three stages of experiments, detecting cataracts in images focused on the eye, iris, and pupil. Overall, 155 experiments were conducted using multiple deep learning algorithms, including ResNet50, ResNet101, YOLOv5, EfficientNetV2, and InceptionV3. We established a minimum threshold of 90 % specificity and sensitivity to ensure the algorithm’s suitability for field use.</div></div><div><h3>Results</h3><div>The cataract detection model for eye-focused images achieved 51.9 % sensitivity and 87.6 % specificity, while the model for iris-focused images, using a good/bad iris filter, achieved 52.4 % sensitivity and 93.3 % specificity. The classification model for segmented-pupil images, employing a good/bad pupil filter with UNet-based semantic segmentation model and EfficientNetV2, yielded 96 % sensitivity and 97 % specificity. Field testing with 302 beneficiaries (604 images) showed an overall sensitivity of 86.6 %, specificity of 93.3 %, positive predictive value of 58.4 %, and negative predictive value of 98.5 %.</div></div><div><h3>Conclusion</h3><div>This paper details the development of an AI mobile app designed to facilitate community screening for cataracts by frontline health workers.</div></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 5","pages":"Article 100094"},"PeriodicalIF":3.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142071889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of ophthalmology education in the era of generative artificial intelligence","authors":"Anna Heinke ,&nbsp;Niloofar Radgoudarzi ,&nbsp;Bonnie B. Huang ,&nbsp;Sally L. Baxter","doi":"10.1016/j.apjo.2024.100089","DOIUrl":"10.1016/j.apjo.2024.100089","url":null,"abstract":"<div><h3>Purpose</h3><p>To explore the integration of generative AI, specifically large language models (LLMs), in ophthalmology education and practice, addressing their applications, benefits, challenges, and future directions.</p></div><div><h3>Design</h3><p>A literature review and analysis of current AI applications and educational programs in ophthalmology.</p></div><div><h3>Methods</h3><p>Analysis of published studies, reviews, articles, websites, and institutional reports on AI use in ophthalmology. Examination of educational programs incorporating AI, including curriculum frameworks, training methodologies, and evaluations of AI performance on medical examinations and clinical case studies.</p></div><div><h3>Results</h3><p>Generative AI, particularly LLMs, shows potential to improve diagnostic accuracy and patient care in ophthalmology. Applications include aiding in patient, physician, and medical students’ education. However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment. Studies revealed varying levels of accuracy of LLMs on ophthalmology board exam questions, underscoring the need for more reliable AI integration. Several educational programs nationwide provide AI and data science training relevant to clinical medicine and ophthalmology.</p></div><div><h3>Conclusions</h3><p>Generative AI and LLMs offer promising advancements in ophthalmology education and practice. Addressing challenges through comprehensive curricula that include fundamental AI principles, ethical guidelines, and updated, unbiased training data is crucial. Future directions include developing clinically relevant evaluation metrics, implementing hybrid models with human oversight, leveraging image-rich data, and benchmarking AI performance against ophthalmologists. Robust policies on data privacy, security, and transparency are essential for fostering a safe and ethical environment for AI applications in ophthalmology.</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 4","pages":"Article 100089"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000902/pdfft?md5=c732bd60e43fb37cd5168b2517837774&pid=1-s2.0-S2162098924000902-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141970533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Upholding artificial intelligence transparency in ophthalmology: A call for collaboration between academia, industry, and government for patient care in the 21st century","authors":"Alexandra Thaler,&nbsp;Joshua Ong,&nbsp;Lama A. Al-Aswad","doi":"10.1016/j.apjo.2024.100093","DOIUrl":"10.1016/j.apjo.2024.100093","url":null,"abstract":"","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 4","pages":"Article 100093"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S216209892400094X/pdfft?md5=e9714c7ce326049bd067f564c27d2d22&pid=1-s2.0-S216209892400094X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142003495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on “Update on coronavirus disease 2019: Ophthalmic manifestations and adverse reactions to vaccination”","authors":"","doi":"10.1016/j.apjo.2024.100079","DOIUrl":"10.1016/j.apjo.2024.100079","url":null,"abstract":"","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 4","pages":"Article 100079"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S216209892400080X/pdfft?md5=b94554e4ffe0c934547ebaa4c3d03c84&pid=1-s2.0-S216209892400080X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141282834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of saliency maps in enhancing ophthalmologists’ trust in artificial intelligence models","authors":"Carolyn Yu Tung Wong ,&nbsp;Fares Antaki ,&nbsp;Peter Woodward-Court ,&nbsp;Ariel Yuhan Ong ,&nbsp;Pearse A. Keane","doi":"10.1016/j.apjo.2024.100087","DOIUrl":"10.1016/j.apjo.2024.100087","url":null,"abstract":"<div><h3>Purpose</h3><p>Saliency maps (SM) allow clinicians to better understand the opaque decision-making process in artificial intelligence (AI) models by visualising the important features responsible for predictions. This ultimately improves interpretability and confidence. In this work, we review the use case for SMs, exploring their impact on clinicians’ understanding and trust in AI models. We use the following ophthalmic conditions as examples: (1) glaucoma, (2) myopia, (3) age-related macular degeneration, and (4) diabetic retinopathy.</p></div><div><h3>Method</h3><p>A multi-field search on MEDLINE, Embase, and Web of Science was conducted using specific keywords. Only studies on the use of SMs in glaucoma, myopia, AMD, or DR were considered for inclusion.</p></div><div><h3>Results</h3><p>Findings reveal that SMs are often used to validate AI models and advocate for their adoption, potentially leading to biased claims. Overlooking the technical limitations of SMs, and the conductance of superficial assessments of their quality and relevance, was discerned. Uncertainties persist regarding the role of saliency maps in building trust in AI. It is crucial to enhance understanding of SMs' technical constraints and improve evaluation of their quality, impact, and suitability for specific tasks. Establishing a standardised framework for selecting and assessing SMs, as well as exploring their relationship with other reliability sources (e.g. safety and generalisability), is essential for enhancing clinicians' trust in AI.</p></div><div><h3>Conclusion</h3><p>We conclude that SMs are not beneficial for interpretability and trust-building purposes in their current forms. Instead, SMs may confer benefits to model debugging, model performance enhancement, and hypothesis testing (e.g. novel biomarkers).</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 4","pages":"Article 100087"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000884/pdfft?md5=947c519db2eaf73afca2cbfe4639495c&pid=1-s2.0-S2162098924000884-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician","authors":"William Rojas-Carabali ,&nbsp;Rajdeep Agrawal ,&nbsp;Laura Gutierrez-Sinisterra ,&nbsp;Sally L. Baxter ,&nbsp;Carlos Cifuentes-González ,&nbsp;Yap Chun Wei ,&nbsp;John Abisheganaden ,&nbsp;Palvannan Kannapiran ,&nbsp;Sunny Wong ,&nbsp;Bernett Lee ,&nbsp;Alejandra de-la-Torre ,&nbsp;Rupesh Agrawal","doi":"10.1016/j.apjo.2024.100084","DOIUrl":"10.1016/j.apjo.2024.100084","url":null,"abstract":"<div><p>Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language, enabling computers to understand, generate, and derive meaning from human language. NLP's potential applications in the medical field are extensive and vary from extracting data from Electronic Health Records –one of its most well-known and frequently exploited uses– to investigating relationships among genetics, biomarkers, drugs, and diseases for the proposal of new medications. NLP can be useful for clinical decision support, patient monitoring, or medical image analysis. Despite its vast potential, the real-world application of NLP is still limited due to various challenges and constraints, meaning that its evolution predominantly continues within the research domain. However, with the increasingly widespread use of NLP, particularly with the availability of large language models, such as ChatGPT, it is crucial for medical professionals to be aware of the status, uses, and limitations of these technologies.</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 4","pages":"Article 100084"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000859/pdfft?md5=4d1793e4d147d08d7a1dbc7d3ffdca4e&pid=1-s2.0-S2162098924000859-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141765063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diversity, equity and inclusion in curriculum vitae for medical and surgical specialty training college entrance","authors":"Thomas Muecke ,&nbsp;Eiman Usmani ,&nbsp;Stephen Bacchi,&nbsp;Robert J. Casson,&nbsp;Weng Onn Chan","doi":"10.1016/j.apjo.2024.100080","DOIUrl":"10.1016/j.apjo.2024.100080","url":null,"abstract":"","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 4","pages":"Article 100080"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000811/pdfft?md5=6077f5383344802042eca893085dc17b&pid=1-s2.0-S2162098924000811-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141733454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}