Journal of medical artificial intelligence最新文献

{"title":"Defining functional requirements for a patient-centric computerized glaucoma treatment and care ecosystem","authors":"N. Goldmann, S. Skalicky, R. Weinreb, R. P. Paletta Guedes, C. Baudouin, Xiulan Zhang, Aukje van Gestel, E. Blumenthal, P. Kaufman, R. Rothman, Ana Maria Vasquez, P. Harasymowycz, D. Welsbie, I. Goldberg","doi":"10.21037/jmai-22-33","DOIUrl":"https://doi.org/10.21037/jmai-22-33","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46553171","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}

{"title":"Enabling scalable clinical interpretation of machine learning (ML)-based phenotypes using real world data","authors":"O. Parsons, N. Barlow, J. Baxter, K. Paraschin, Andrea Derix, Peter Hein, R. Dürichen","doi":"10.21037/jmai-22-42","DOIUrl":"https://doi.org/10.21037/jmai-22-42","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41562490","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}

{"title":"Artificial intelligence in screening for obstructive sleep apnoea syndrome (OSAS): a narrative review","authors":"Bei Pei, Ming Xia, Hong Jiang","doi":"10.21037/jmai-22-79","DOIUrl":"https://doi.org/10.21037/jmai-22-79","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42763342","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}

{"title":"Using a self-attention architecture to automate valence categorization of French teenagers' free descriptions of their family relationships. A proof of concept.","authors":"M. Sedki, N. Vidal, P. Roux, C. Barry, M. Speranza, B. Falissard, E. Brunet-Gouet","doi":"10.1101/2023.01.16.23284557","DOIUrl":"https://doi.org/10.1101/2023.01.16.23284557","url":null,"abstract":"This paper proposes a proof of concept of using natural language processing techniques to categorize valence of family relationships described in free texts written by french teenagers. The proposed study traces the evolution of techniques for word embedding. After decomposing the different texts in our possession into short texts composed of sentences and manual labeling, we tested different word embedding scenarios to train a multi-label classification model where a text can take several labels: labels describing the family link between the teenager and the person mentioned in the text and labels describing the teenager's relationship with them positive/negative/neutral valence). The natural baseline for word vector representation of our texts is to build a TF-IDF and train classical classifiers (Elasticnet logistic regression, gradient boosting, random forest, support vector classifier) after selecting a model by cross validation in each class of machine learning models. We then studied the strengths of word-vectors embeddings by an advanced language representation technique via the CamemBERT transformer model, and, again, used them with classical classifiers to compare their respective performances. The last scenario consisted in augmenting the CamemBERT with output dense layers (perceptron) representing a classifier adapted to the multi-label classification and fine-tuning the CamemBERT original layers. The optimal fine-tuning depth that achieves a bias-variance trade-off was obtained by a cross-validation procedure. The results of the comparison of the three scenarios on a test dataset show a clear improvement of the classification performances of the scenario with fine-tuning beyond the baseline and of a simple vectorization using CamemBERT without fine-tuning. Despite the moderate size of the dataset and the input texts, fine-tuning to an optimal depth remains the best solution to build a classifier.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45085579","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}

{"title":"Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators","authors":"B. Schouten, M. Schinkel, A. W. Boerman, Petra van Pijkeren, Maureen Thodé, M. V. van Beneden, R. N. Nannan Panday, R. de Jonge, W. Wiersinga, P. Nanayakkara","doi":"10.21037/jmai-22-71","DOIUrl":"https://doi.org/10.21037/jmai-22-71","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47724594","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}

{"title":"Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.","authors":"Ebraham Alskaf, Utkarsh Dutta, Cian M Scannell, Amedeo Chiribiri","doi":"10.21037/jmai-22-36","DOIUrl":"10.21037/jmai-22-36","url":null,"abstract":"<p><strong>Background: </strong>The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging.</p><p><strong>Methods: </strong>The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau², I² and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach.</p><p><strong>Results: </strong>A total of 81 studies met the inclusion criteria. The most common imaging modality was coronary computed tomography angiography (CCTA) (58%) and the most common deep learning method was convolutional neural network (CNN) (52%). The majority of studies demonstrated good performance metrics. The most common outputs were focused on coronary artery segmentation, clinical outcome prediction, coronary calcium quantification and FFR prediction, and most studies reported area under the curve (AUC) of ≥80%. The pooled diagnostic odds ratio (DOR) derived from 8 studies looking at FFR prediction using CCTA was 12.5 using the Mantel-Haenszel (MH) method. There was no significant heterogeneity amongst studies according to Q test (P=0.2496).</p><p><strong>Conclusions: </strong>Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.</p>","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"5 ","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b7/84/EMS163415.PMC7614252.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10826937","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":"Sleep’s depth detection using electroencephalogram signal processing and neural network classification","authors":"M. Touil, L. Bahatti, A. El Magri","doi":"10.21037/jmai-22-32","DOIUrl":"https://doi.org/10.21037/jmai-22-32","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44602407","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}

{"title":"Accuracy of predicting IgHV mutation status in chronic lymphocytic leukemia using RNA expression profiling and machine learning","authors":"A. Charifa, Hong Zhang, A. Pecora, A. Ip, I. De Dios, Wanlong Ma, L. Leslie, T. Feldman, A. Goy, M. Albitar","doi":"10.21037/jmai-22-28","DOIUrl":"https://doi.org/10.21037/jmai-22-28","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47427615","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}

{"title":"A narrative review on radiotherapy practice in the era of artificial intelligence: how relevant is the medical physicist?","authors":"Eric Naab Manson, A. N. Mumuni, E. Fiagbedzi, I. Shirazu, H. Sulemana","doi":"10.21037/jmai-22-27","DOIUrl":"https://doi.org/10.21037/jmai-22-27","url":null,"abstract":"Background and Objective: Artificial intelligence (AI) uses computers and machines to simulate how the human mind makes decisions and solves problems. In radiotherapy practice, AI technologies continue to be promising in image registration, synthetic computed tomography (CT), image segmentation, motion management, treatment planning, and delivery procedures, patient follow-up and quality assurance (QA). This, therefore, provides a new window of opportunity to improve upon the accuracy and output times of the manual implementation of these procedures. The goal of this review was to explore how machine learning AI technologies in radiotherapy could affect the clinical practice of medical physicists. Methods: A narrative literature review was conducted from PubMed, Science Direct and Scopus using the search terms: in the English language within 6 months. Key Content and Findings: The roles of AI and the clinical medical physicist are complementary in radiotherapy practice. Both the medical physicists and AI technology are highly needed to support the full implementation and optimization of radiotherapy procedures. Conclusions: To achieve successful implementation of AI in radiotherapy and optimize radiotherapy procedures, clinical medical physicist should receive some compulsory training in AI technologies during their education and training. They should ultimately be involved in the incorporation of machine learning technologies in radiotherapy equipment. patient-specific dosimetric of patient treatment Dosimetric measurements in phantoms one of following detectors: portal imaging The third type of looks for delivery errors in log files generated during during delivery time-series linear quality","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47113877","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}

{"title":"Machine learning in atrial fibrillation—racial bias and a call for caution","authors":"Hiten Doshi, J. Chudow, K. Ferrick, A. Krumerman","doi":"10.21037/jmai-21-12","DOIUrl":"https://doi.org/10.21037/jmai-21-12","url":null,"abstract":"© Journal of Medical Artificial Intelligence. All rights reserved. J Med Artif Intell 2021;4:6 | https://dx.doi.org/10.21037/jmai-21-12 Early diagnosis of atrial fibrillation (AF), a common arrhythmia that can cause adverse events such as stroke, is a major clinical challenge. Due to its often asymptomatic and paroxysmal nature, AF is easily missed on single electrocardiograms (ECGs), making outpatient screening challenging. As a result, patients may not receive a timely diagnosis, with up to 5% of all AF cases being diagnosed at the time of stroke (1). Various machine learning (ML) models, primarily involving supervised ML methods, have been developed with the hopes of bringing an effective population screening tool to the forefront. While these models show strong performance in their respective studies, data regarding their effectiveness across racial groups is lacking. Therefore, using ML for AF screening requires two important considerations: (I) any biases in the training set data will be perpetuated in the predictions that the models offer; (II) AF has a known racial paradox, where traditional risk factors that were derived from a largely Caucasian population have a weaker correlation with AF incidence in Black patients. Below, we elaborate on these points and argue that while ML presents a unique opportunity to increase the detection of AF, it also deserves special caution to avoid reinforcing existing healthcare disparities. ML AF screening tools are commonly developed using ECG data about p-waves, R-R intervals, heart rate, and other parameters. While this has shown the ability to produce strong predictive models, the actual data sources deserve scrutiny (2). A recently published systematic review identified that while more than 100 publications exist using ECG data to develop ML models, more than half of them used the same four open-access ECG databases (3). In theory, this is not necessarily problematic, and it is understandable that so many studies reuse well known and freely available datasets. Ideally, however, the datasets would report a sufficient level of patient diversity to well represent the entire US population. Instead, many of the most commonly used ECG datasets only report limited demographic data, including the patient’s age, gender, and/or baseline clinical characteristics, without reporting racial or ethnic background. Considering the known racial differences that exist in several baseline ECG parameters, including left ventricular hypertrophy, right axis deviation, bundle branch blocks, and others, transparency about racial demographic information in these datasets is critical (4). Table 1 summarizes the most commonly used ECG databases, as well as the readily available demographic information provided by each. The reuse of these datasets carries particular concern in the diagnosis of AF, a disease with a known “racial paradox”. This paradox refers to the fact that while Black patients have a higher burden of AF risk f","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42700356","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}