NPJ Digital Medicine最新文献_第8页

Developing a named entity framework for thyroid cancer staging and risk level classification using large language models

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-01 DOI: 10.1038/s41746-025-01528-y

Matrix M. H. Fung, Eric H. M. Tang, Tingting Wu, Yan Luk, Ivan C. H. Au, Xiaodong Liu, Victor H. F. Lee, Chun Ka Wong, Zhili Wei, Wing Yiu Cheng, Isaac C. Y. Tai, Joshua W. K. Ho, Jason W. H. Wong, Brian H. H. Lang, Kathy S. M. Leung, Zoie S. Y. Wong, Joseph T. Wu, Carlos K. H. Wong

{"title":"Developing a named entity framework for thyroid cancer staging and risk level classification using large language models","authors":"Matrix M. H. Fung, Eric H. M. Tang, Tingting Wu, Yan Luk, Ivan C. H. Au, Xiaodong Liu, Victor H. F. Lee, Chun Ka Wong, Zhili Wei, Wing Yiu Cheng, Isaac C. Y. Tai, Joshua W. K. Ho, Jason W. H. Wong, Brian H. H. Lang, Kathy S. M. Leung, Zoie S. Y. Wong, Joseph T. Wu, Carlos K. H. Wong","doi":"10.1038/s41746-025-01528-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01528-y","url":null,"abstract":"We developed a named entity (NE) framework for information extraction from semi-structured clinical notes retrieved from The Cancer Genome Atlas—Thyroid Cancer (TCGA-THCA) database and examined Large Language Models (LLMs) strategies to classify the 8th edition of American Joint Committee on Cancer (AJCC) staging and American Thyroid Association (ATA) risk category for patients with well-differentiated thyroid cancer. The NE framework consisted of annotation guidelines development, ground truth labelling, prompting approaches, and evaluation codes. Four LLMs (Mistral-7B-Instruct, Llama-3.1-8B-Instruct, Gemma-2-9B-Instruct, and Qwen2.5-7B-Instruct) were offline utilised for information extraction, comparing with expert-curated ground truth. Our framework was developed using 50 TCGA-THCA pathology notes. 289 TCGA-THCA notes and 35 pseudo-clinical cases were used for validation. Taking an ensemble-like majority-vote strategy achieved satisfactory performance for AJCC and ATA in both development and validation sets. Our framework and ensemble classifier optimised efficiency and accuracy of classifying stage and risk category in thyroid cancer patients.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"130 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural language processing of electronic health records for early detection of cognitive decline: a systematic review

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-01 DOI: 10.1038/s41746-025-01527-z

Ravi Shankar, Anjali Bundele, Amartya Mukhopadhyay

引用次数: 0

Value of clinical review for AI-guided deep vein thrombosis diagnosis with ultrasound imaging by non-expert operators

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-01 DOI: 10.1038/s41746-025-01518-0

Giancarlo Speranza, Sven Mischkewitz, Fouad Al-Noor, Bernhard Kainz

{"title":"Value of clinical review for AI-guided deep vein thrombosis diagnosis with ultrasound imaging by non-expert operators","authors":"Giancarlo Speranza, Sven Mischkewitz, Fouad Al-Noor, Bernhard Kainz","doi":"10.1038/s41746-025-01518-0","DOIUrl":"https://doi.org/10.1038/s41746-025-01518-0","url":null,"abstract":"Deep vein thrombosis (DVT) carries high morbidity, mortality, and costs globally. Point of care ultrasound (POCUS) image acquisition by non-ultrasound-trained providers, supported by an AI-based guidance and remote image review system, is believed to improve the timeliness and cost-effectiveness of diagnosis. We examine a database of 381 patients with suspected DVT who underwent an AI-guided ultrasound scan by a non-ultrasound-trained nurse and an expert sonographer-performed standard compression ultrasound scan. Each AI-guided scan was reviewed remotely by blinded radiologists or blinded independent POCUS-certified American Emergency Medicine (EM) physicians. Remote reviewer and standard scan diagnoses were compared. The primary endpoint is AI-guidance system sensitivity with clinician review; secondary endpoints include specificity, positive predictive value, negative predictive value, image quality, inter-observer image quality, and vein compressibility agreement. Data was analysed through the bootstrapping method, bootstrapping with a second reader for each scan, and a majority voting system. Eighty percent (n = 304) of scans were of sufficient diagnostic quality. Radiologist reviewer sensitivity ranged from 90%–95%, specificity from 74%–84%, NPV from 98%–99%, PPV from 30%–42%, and potential expert-led ultrasound scans avoided from 39%–50%. Inter-observer agreement for image quality was 0.15 and for compressibility 0.61. EM reviewer sensitivity ranged from 95%–98%, specificity from 97%–100%, NPV was 99%, PPV from 81%–100%, and potential expert-led ultrasound scans avoided from 29%–38%. Inter-observer agreement for image quality was 0.59 and for compressibility 0.67. Diagnosing lower extremity DVT through AI-guided image acquisition with clinician review is feasible. Performance is influenced by reviewer expertise. We find potential positive impacts on health economics, including safely avoiding expert-led ultrasound scans.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"52 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transfer learning method for prenatal ultrasound diagnosis of biliary atresia

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-28 DOI: 10.1038/s41746-025-01525-1

Fujiao He, Gang Li, Zhichao Zhang, Chaoran Yang, Zeyu Yang, Hao Ding, Dan Zhao, Wei Sun, Yu Wang, Kaihui Zeng, Xian Li, Mingming Shao, Jiao Yin, Jia Yao, Boxuan Hong, Zhibo Zhang, Zhengwei Yuan, Zongjie Weng, Luyao Zhou, Mo Zhang, Lizhu Chen

{"title":"Transfer learning method for prenatal ultrasound diagnosis of biliary atresia","authors":"Fujiao He, Gang Li, Zhichao Zhang, Chaoran Yang, Zeyu Yang, Hao Ding, Dan Zhao, Wei Sun, Yu Wang, Kaihui Zeng, Xian Li, Mingming Shao, Jiao Yin, Jia Yao, Boxuan Hong, Zhibo Zhang, Zhengwei Yuan, Zongjie Weng, Luyao Zhou, Mo Zhang, Lizhu Chen","doi":"10.1038/s41746-025-01525-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01525-1","url":null,"abstract":"Biliary atresia (BA) is a rare and severe congenital disorder with a significant challenge for prenatal diagnosis. This study, registered at the Chinese Clinical Trial Registry (ChiCTR2200059705), aimed to develop an intelligent model to aid in the prenatal diagnosis of BA. To develop and evaluate this model, fetuses from 20 hospitals across China and infants sourced from public database were collected. The transfer-learning model (TLM) demonstrated superior diagnostic performance compared to the basic deep-learning model, with higher area under the curves of 0.906 (95%CI: 0.872–0.940) vs 0.793 (0.743–0.843), 0.914 (0.875–0.953) vs 0.790 (0.727–0.853), and 0.907 (0.869–0.945) vs 0.880 (0.838–0.922) for the three independent test cohorts. Furthermore, when aided by the TLM, diagnostic accuracy surpassed that of individual sonologists alone. The TLM achieved satisfactory performance in predicting fetal BA, providing a low-cost, easily accessible, and accurate diagnostic tool for this condition, making it an effective aid in clinical practice.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"7 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A scoping review of ethical aspects of public-private partnerships in digital health

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-27 DOI: 10.1038/s41746-025-01515-3

Marieke A. R. Bak, Daan Horbach, Alena Buyx, Stuart McLennan

引用次数: 0

A hybrid multi model artificial intelligence approach for glaucoma screening using fundus images

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-27 DOI: 10.1038/s41746-025-01473-w

Parmanand Sharma, Naoki Takahashi, Takahiro Ninomiya, Masataka Sato, Takehiro Miya, Satoru Tsuda, Toru Nakazawa

{"title":"A hybrid multi model artificial intelligence approach for glaucoma screening using fundus images","authors":"Parmanand Sharma, Naoki Takahashi, Takahiro Ninomiya, Masataka Sato, Takehiro Miya, Satoru Tsuda, Toru Nakazawa","doi":"10.1038/s41746-025-01473-w","DOIUrl":"https://doi.org/10.1038/s41746-025-01473-w","url":null,"abstract":"Glaucoma, a leading cause of blindness, requires accurate early detection. We present an AI-based Glaucoma Screening (AI-GS) network comprising six lightweight deep learning models (total size: 110 MB) that analyze fundus images to identify early structural signs such as optic disc cupping, hemorrhages, and nerve fiber layer defects. The segmentation of the optic cup and disc closely matches that of expert ophthalmologists. AI-GS achieved a sensitivity of 0.9352 (95% CI 0.9277–0.9435) at 95% specificity. In real-world testing, sensitivity dropped to 0.5652 (95% CI 0.5218–0.6058) at ~0.9376 specificity (95% CI 0.9174–0.9562) for the standalone binary glaucoma classification model, whereas the full AI-GS network maintained higher sensitivity (0.8053, 95% CI 0.7704–0.8382) with good specificity (0.9112, 95% CI 0.8887–0.9356). The sub-models in AI-GS, with enhanced capabilities in detecting early glaucoma-related structural changes, drive these improvements. With low computational demands and tunable detection parameters, AI-GS promises widespread glaucoma screening, portable device integration, and improved understanding of disease progression.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"36 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning based ultrasound diagnostic tool driven by 3D visualization of thyroid nodules

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-27 DOI: 10.1038/s41746-025-01455-y

Yahan Zhou, Chen Chen, Jincao Yao, Jiabin Yu, Bojian Feng, Lin Sui, Yuqi Yan, Xiayi Chen, Yuanzhen Liu, Xiao Zhang, Hui Wang, Qianmeng Pan, Weijie Zou, Qi Zhang, Lu Lin, Chenke Xu, Shengxing Yuan, Qingquan He, Xiaofan Ding, Ping Liang, Vicky Yang Wang, Dong Xu

{"title":"A deep learning based ultrasound diagnostic tool driven by 3D visualization of thyroid nodules","authors":"Yahan Zhou, Chen Chen, Jincao Yao, Jiabin Yu, Bojian Feng, Lin Sui, Yuqi Yan, Xiayi Chen, Yuanzhen Liu, Xiao Zhang, Hui Wang, Qianmeng Pan, Weijie Zou, Qi Zhang, Lu Lin, Chenke Xu, Shengxing Yuan, Qingquan He, Xiaofan Ding, Ping Liang, Vicky Yang Wang, Dong Xu","doi":"10.1038/s41746-025-01455-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01455-y","url":null,"abstract":"Recognizing the limitations of computer-assisted tools for thyroid nodule diagnosis using static ultrasound images, this study developed a diagnostic tool utilizing dynamic ultrasound video, namely Thyroid Nodules Visualization (TNVis), by leveraging a two-stage deep learning framework that involved three-dimensional (3D) visualization. In this multicenter study, 4569 cases were included for framework development, and data from seven hospitals were employed for diagnostic validation. TNVis achieved a Dice similarity coefficient of 0.90 after internal testing. For the external validation, TNVis significantly improved radiologists’ performance, reaching an AUC of 0.79, compared to their diagnostic performance without the use of TNVis (AUC: 0.66; p < 0.001) and those with partial assistance (AUC: 0.72; p < 0.001). In conclusion, the TNVis-assisted diagnostic strategy not only significantly improves the diagnostic ability of radiologists but also closely imitates their clinical diagnostic procedures and provides them with an objective 3D representation of the nodules for precise and personalized diagnosis and treatment planning.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"28 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143506975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continuous multimodal data supply chain and expandable clinical decision support for oncology

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-27 DOI: 10.1038/s41746-025-01508-2

Jee Suk Chang, Hyunwook Kim, Eun Sil Baek, Jeong Eun Choi, Joon Seok Lim, Jin Sung Kim, Sang Joon Shin

{"title":"Continuous multimodal data supply chain and expandable clinical decision support for oncology","authors":"Jee Suk Chang, Hyunwook Kim, Eun Sil Baek, Jeong Eun Choi, Joon Seok Lim, Jin Sung Kim, Sang Joon Shin","doi":"10.1038/s41746-025-01508-2","DOIUrl":"https://doi.org/10.1038/s41746-025-01508-2","url":null,"abstract":"The study introduces a clinical decision support system (CDSS) developed at a single academic cancer center, integrating real-time clinical, genomic, and imaging data for over 170,000 patients across 11 cancer types. We have developed the Yonsei Cancer Data Library (YCDL) data integration framework to continuously collect and update multimodal datasets comprising over 800 features per case. Quality control measures, using 143 logical comparisons, addressed missing data and outliers, achieving median accuracies of 92.6% for surgical and 98.7% for molecular pathology. An Extract-Transform-Load (ETL) process with natural language processing transformed unstructured data, enabling survival analyses stratified by tumor stage, which revealed significant stage-dependent differences. The CDSS dashboard visualizes patient trajectories and key milestones. User feedback from oncology professionals showed strong acceptance, with satisfaction scores exceeding 4 out of 5. This framework demonstrates the potential of multimodal data integration to enhance clinical decision-making and patient outcomes, with future research needed to validate its generalizability and scalability.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"210 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143507035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of individual treatment allocation between electroconvulsive therapy or ketamine using the Personalized Advantage Index

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-27 DOI: 10.1038/s41746-025-01523-3

Benjamin S. C. Wade, Ryan Pindale, James Luccarelli, Shuang Li, Robert C. Meisner, Stephen J. Seiner, Joan A. Camprodon, Michael E. Henry

{"title":"Prediction of individual treatment allocation between electroconvulsive therapy or ketamine using the Personalized Advantage Index","authors":"Benjamin S. C. Wade, Ryan Pindale, James Luccarelli, Shuang Li, Robert C. Meisner, Stephen J. Seiner, Joan A. Camprodon, Michael E. Henry","doi":"10.1038/s41746-025-01523-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01523-3","url":null,"abstract":"Electroconvulsive therapy (ECT) and ketamine are effective treatments for depression; however, evidence-based guidelines are needed to inform individual treatment selection. We adapted the Personalized Advantage Index (PAI) using machine learning to predict optimal treatment assignment to ECT or ketamine using EHR data on 2506 ECT and 196 ketamine patients. Depressive symptoms were evaluated using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Propensity score matching across treatments was used to address confounding by indication, yielding a sample of 392 patients (n = 196 per treatment). Models predicted differential minimum QIDS scores (min-QIDS) over acute treatment using pretreatment EHR measures and SHAP values identified prescriptive predictors. Patients with large PAI scores who received a predicted optimal had significantly lower min-QIDS compared to the non-optimal treatment group (mean difference = 1.19 [95% CI: 0.32, ∞], t = 2.25, q < 0.05, d = 0.26). Our model identified candidate pretreatment factors to provide actionable, effective antidepressant treatment selection guidelines.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"29 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143506974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalist medical AI reimbursement challenges and opportunities

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-26 DOI: 10.1038/s41746-025-01521-5

Arjun Mahajan, Dylan Powell

引用次数: 0