BJR|Artificial Intelligence最新文献

{"title":"AI-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence","authors":"Konstantinos Vrettos, Matthaios Triantafyllou, K. Marias, A. H. Karantanas, M. Klontzas","doi":"10.1093/bjrai/ubae011","DOIUrl":"https://doi.org/10.1093/bjrai/ubae011","url":null,"abstract":"\u0000 The advent of radiomics has revolutionized medical image analysis, affording the extraction of high dimensional quantitative data for the detailed examination of normal and abnormal tissues. Artificial intelligence (AI) can be used for the enhancement of a series of steps in the radiomics pipeline, from image acquisition and preprocessing, to segmentation, feature extraction, feature selection and model development. The aim of this review is to present the most used AI methods for radiomics analysis, explaining the advantages and limitations of the methods. Some of the most prominent AI architectures mentioned in this review include Boruta, random forests, gradient boosting, generative adversarial networks, convolutional neural networks, and transformers. Employing these models in the process of radiomics analysis can significantly enhance the quality and effectiveness of the analysis, while addressing several limitations that can reduce the quality of predictions. Addressing these limitations can enable high quality clinical decisions and wider clinical adoption. Importantly, this review will aim to highlight how AI can assist radiomics in overcoming major bottlenecks in clinical implementation, ultimately improving the translation potential of the method.","PeriodicalId":518592,"journal":{"name":"BJR|Artificial Intelligence","volume":"139 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655988","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":"Translating potential improvement in the precision and accuracy of lung nodule measurements on computed tomography scans by software derived from artificial intelligence into impact on clinical practice—a simulation study","authors":"Mubarak Patel, Peter Auguste, Jason Madan, Hesam Ghiasvand, Julia Geppert, Asra Asgharzadeh, Emma Helm, Yen-Fu Chen, Daniel Gallacher","doi":"10.1093/bjrai/ubae010","DOIUrl":"https://doi.org/10.1093/bjrai/ubae010","url":null,"abstract":"\u0000 \u0000 \u0000 Accurate measurement of lung nodules is pivotal to lung cancer detection and management. Nodule size forms the main basis of risk categorisation in existing guidelines. However, measurements can be highly variable between manual readers. This paper explores the impact of potentially improved nodule size measurement assisted by generic artificial intelligence (AI)-derived software on clinical management compared with manual measurement.\u0000 \u0000 \u0000 \u0000 The simulation study created a baseline cohort of people with lung nodules, guided by nodule size distributions reported in the literature. Precision and accuracy were simulated to emulate measurement of nodule size by radiologists with and without the assistance of AI-derived software and by the software alone. Nodule growth was modelled over a 4-year time frame, allowing evaluation of management strategies based on existing clinical guidelines.\u0000 \u0000 \u0000 \u0000 Measurement assisted by AI-derived software increased cancer detection compared to an unassisted radiologist for a combined solid and sub-solid nodule population (62.5% vs 61.4%). AI-assisted measurement also correctly identified more benign nodules (95.8% vs 95.4%), however it was associated with over an additional month of surveillance on average (5.12 vs 3.95 months). On average, with AI assistance people with cancer are diagnosed faster, and people without cancer are monitored longer.\u0000 \u0000 \u0000 \u0000 In this simulation, the potential benefits of improved accuracy and precision associated with AI-based diameter measurement is associated with additional monitoring of non-cancerous nodules. AI may offer additional benefits not captured in this simulation, and it is important to generate data supporting these, and adjust guidelines as necessary.\u0000 \u0000 \u0000 \u0000 This paper shows the effects of greater measurement accuracy associated with AI assistance compared with unassisted measurement.\u0000","PeriodicalId":518592,"journal":{"name":"BJR|Artificial Intelligence","volume":"165 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375910","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":"Adoption, orchestration and deployment of AI within the NHS—facilitators and barriers: an expert roundtable discussion","authors":"Gerald Lip, A. Novak, Mathias Goyen, Katherine Boylan, Amrita Kumar","doi":"10.1093/bjrai/ubae009","DOIUrl":"https://doi.org/10.1093/bjrai/ubae009","url":null,"abstract":"\u0000 Clinical care, workflow, and patient outcomes within NHS radiology services will potentially benefit significantly in coming years with the integration of artificial intelligence (AI) into various specialty areas. This article summarises an expert roundtable discussion, convened by the British Institute of Radiology, comprised of experts from clinical, NHS management and industry backgrounds. Facilitators and barriers to both implementation and integration of AI platforms into daily practice are discussed alongside examples including AI in emergency care and breast screening. Issues addressed include the need to generate more real-world evidence that helps developers and service providers to determine the optimal way for patients to reap the benefits of AI with faster reporting turnaround time, faster diagnoses and treatment times, for example. To ensure the effective and timely widescale implementation, adoption requires a national policy drive and national level coordination but specific, local needs of NHS Trusts and any particular service within Trusts will also need to be met. By embedding AI-capabilities into scanners, ultrasound-devices, X-ray equipment, and PACS-systems, local services will benefit, while AI applications can streamline workflows across and between departments and potentially Trusts. AI predictive analytics and systems engineering can be used to better manage patient volume and movement. In the short term, education, training, and confidence in adapting and adopting AI in radiology require dedicated local, national and governmental effort but the future benefits promise to justify the means.","PeriodicalId":518592,"journal":{"name":"BJR|Artificial Intelligence","volume":"45 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141384545","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":"Synthetic data in radiological imaging: Current state and future outlook","authors":"E. Sizikova, A. Badal, Jana G. Delfino, Miguel Lago, Brandon Nelson, Niloufar Saharkhiz, B. Sahiner, Ghada Zamzmi, Aldo Badano","doi":"10.1093/bjrai/ubae007","DOIUrl":"https://doi.org/10.1093/bjrai/ubae007","url":null,"abstract":"\u0000 A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and representative patient datasets with appropriate annotations may be burdensome due to high acquisition cost, safety limitations, patient privacy restrictions or low disease prevalence rates. In silico data offers a number of potential advantages to patient data, such as diminished patient harm, reduced cost, simplified data acquisition, scalability, improved quality assurance testing, and a mitigation approach to data imbalances. We summarize key research trends and practical uses for synthetically generated data for radiological applications of AI. Specifically, we discuss different types of techniques for generating synthetic examples, their main application areas, and related quality control assessment issues. We also discuss current approaches for evaluating synthetic imaging data. Overall, synthetic data holds great promise in addressing current data availability gaps, but additional work is needed before its full potential is realized.","PeriodicalId":518592,"journal":{"name":"BJR|Artificial Intelligence","volume":"59 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108377","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":"Does artificial intelligence need companionship to assist in drug discovery? The Kirsten rat sarcoma virus study","authors":"Mourad Stitou, J. Koomen, Denis Imbody, Yi Liao, Andrii Monastyrskyi, U. Rix, Derek R. Duckett, E. Haura, Aleksandra Karolak","doi":"10.1093/bjrai/ubae001","DOIUrl":"https://doi.org/10.1093/bjrai/ubae001","url":null,"abstract":"\u0000 In this Opinion article, we confront the role of artificial intelligence (AI) in targeting and understanding resistance to targeted therapy using the most frequently mutated oncoprotein family in human cancer, rat sarcoma virus guanosine triphosphate hydrolases (RAS GTPases), here Kirsten RAS (KRAS), as an example. Aberrant regulation of the active GTP-bound state of KRAS is associated with tumourigenesis, aggressive disease, and poor prognosis. KRAS mutations (eg, G12C, G12D, G12V, G13D, inter al.) are drivers of numerous cancer types, including non-small cell lung, colorectal, and pancreatic cancers. These mutations have shown to play a significant role in cell behaviour and response to treatment. Since its discovery in the 1980s, it has been recognized that over-expression of KRAS and other RAS family members induces resistance to radiotherapy. Moreover, over the years preclinical and clinical studies showed that tumours with KRAS mutations exhibit different treatment sensitivities compared to tumours with wild-type KRAS.","PeriodicalId":518592,"journal":{"name":"BJR|Artificial Intelligence","volume":"20 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532812","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}