人工智能在肿瘤放射治疗中的研究进展

大数据和人工智能在各行各业方兴未艾,在精准放疗领域也在不断深入。近些年来,放射治疗技术发展迅速,积累了大量的影像和治疗数据,接下来迫切需要进行大数据分析,来协助医生做决策,提高医生和物理师的工作效率,加强放疗过程的质控,因此人工智能在放疗领域应运而生。本文阐述了人工智能在放射治疗各环节中的发展前景和应用情况。     

人工智能在泌尿系统肿瘤中的应用研究进展

近年来,机器学习和神经网络技术的进步使得人工智能(artificial intelligence,AI)在指导临床诊断、治疗和资源投入等方面产生了巨大影响。在泌尿系统肿瘤领域,AI在改善前列腺癌、肾癌和膀胱癌的诊断和治疗方面取得了诸多进步,已可利用机器学习和神经网络技术自动化进行预后预测、治疗计划优化和患者随访教育等。有证据表明,AI指导可以显著降低泌尿系统肿瘤的诊断和治疗管理的主观性。尽管AI在泌尿系统肿瘤中的应用已经成为现代科技的热点,但对比真实世界的医疗决策时,AI仍然存在明显的局限性。通过对AI目前的优势和不足进行概述,旨在为未来AI在泌尿系统肿瘤的精准化、个性化诊治和长期管理中的应用提供参考。     

人工智能在早期食管癌内镜诊断中的应用及进展

中国是食管癌高发国家,中晚期食管癌不仅给患者生命健康带来严重威胁,也给其家庭和社会带来沉重的经济负担。食管癌的早诊早治一直是临床医学研究的热点,而胃镜筛查是食管癌早诊早治的关键,人工智能的发展有望在内镜检查和质控等方面为食管癌早诊早治提供新的辅助手段。文章通过概述人工智能在早期食管癌内镜诊断中的应用和发展,归纳了人工智能在早期食管癌诊断中的研究结果,阐明其应用的重要性,总结了人工智能在早期食管癌内镜诊断中的难点及问题,并对未来人工智能在食管癌内镜诊断中的应用进行展望。     

人工智能用于妇科恶性肿瘤放疗的研究进展北大核心CSCD

妇科恶性肿瘤发病率和死亡率较高,仍需寻找有效的治疗手段以提高患者生存获益。人工智能(AI)旨在通过机器模拟人脑的思考方式,对原始问题进行智能化处理。其在恶性肿瘤的诊疗领域表现出巨大的发展潜力,在妇科恶性肿瘤领域也有所进展。本文概述了AI在妇科恶性肿瘤诊疗领域的应用,并着重介绍AI在妇科恶性肿瘤放疗领域的研究进展;主要聚焦于放疗中自动勾画、剂量预测、放射不良反应及疗效预测等关键环节,探讨了目前AI在妇科恶性肿瘤放疗研究中的优势与不足。     

基于深度学习的人工智能在数字病理学中的进展

全切片数字化图像扫描技术的进步促成了数字病理学的诞生。随着存储技术的提高和互联网技术与计算机技术的迅速发展,深度学习的方法被广泛应用于病理学图像的分析中,其目标是化解病理学图像冗余复杂的信息导致病理学医师诊断和分析困难的问题,减轻病理学医师日常繁琐的分析工作,并提高分析结果的准确度。回顾分析常用于病理学分析的深度学习方法,介绍深度学习在病理学分析中各领域的应用,并讨论深度学习在病理学分析中的挑战和机遇。     

Academics as leaders in the cancer artificial intelligence revolution

The successful translation of artificial intelligence (AI) applications into clinical cancer care practice requires guidance by academic cancer researchers and providers who are well poised to step into leadership roles. In this commentary, the authors describe the landscape of the deep learning-based AI innovation boom in cancer research. For progress in applied AI research to continue, 4 essential components must be present: algorithms, data, computational resources, and domain-specific expertise. Each of these components is available to researchers and providers in academic settings; in particular, cancer care domain-specific expertise in academia is superb. Three common pitfalls for deep learning research also are detailed along with a discussion of how the academic oncology research environment is well suited to guard against these challenges. In this rapidly developing field, there are few established standards, and oncology researchers and providers must educate themselves about emerging AI technology to avoid common pitfalls and ensure responsible use.     

Development of artificial intelligence technology in diagnosis,treatment, and prognosis of colorectal cancer

Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.     

Application of artificial intelligence to the diagnosis and therapy of colorectal cancer

Artificial intelligence (AI) is a relatively new branch of computer science involving many disciplines and technologies, including robotics, speech recognition, natural language and image recognition or processing, and machine learning. Recently, AI has been widely applied in the medical field. The effective combination of AI and big data can provide convenient and efficient medical services for patients. Colorectal cancer (CRC) is a common type of gastrointestinal cancer. The early diagnosis and treatment of CRC are key factors affecting its prognosis. This review summarizes the research progress and clinical application value of AI in the investigation, early diagnosis, treatment, and prognosis of CRC, to provide a comprehensive theoretical basis for AI as a promising diagnostic and treatment tool for CRC.     

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.     

Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy

Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.     

人工智能在内镜诊断上消化道早癌领域的研究进展

随着深度学习算法的不断开发,人工智能在内镜辅助诊断中的应用也备受关注。上消化道肿瘤因其早期不易被发现的特点成为内镜医师镜下诊断的难题,而计算机辅助诊断等新技术有望实现对上消化道早期恶性病变及其相关危险因素的识别及诊断。本文阐述了目前人工智能在内镜诊断上消化道早癌领域的应用情况和发展前景。     

Artificial intelligence and cholangiocarcinoma: Updates and prospects

Artificial intelligence (AI) is the timeliest field of computer science and attempts to mimic cognitive function of humans to solve problems. In the era of “Big data”, there is an ever-increasing need for AI in all aspects of medicine. Cholangiocarcinoma (CCA) is the second most common primary malignancy of liver that has shown an increase in incidence in the last years. CCA has high mortality as it is diagnosed in later stages that decreases effect of surgery, chemotherapy, and other modalities. With technological advancement there is an immense amount of clinicopathologic, genetic, serologic, histologic, and radiologic data that can be assimilated together by modern AI tools for diagnosis, treatment, and prognosis of CCA. The literature shows that in almost all cases AI models have the capacity to increase accuracy in diagnosis, treatment, and prognosis of CCA. Most studies however are retrospective, and one study failed to show AI benefit in practice. There is immense potential for AI in diagnosis, treatment, and prognosis of CCA however limitations such as relative lack of studies in use by human operators in improvement of survival remains to be seen.     

Evaluation of a delineation software for cardiac atlas-based autosegmentation: An example of the use of artificial intelligence in modern radiotherapy

PurposeThe primary objective of this work was to implement and evaluate a cardiac atlas-based autosegmentation technique based on the “Workflow Box” software (Mirada Medical, Oxford UK), in order to delineate cardiac substructures according to European Society of Therapeutic Radiation Oncology (ESTRO) guidelines; review and comparison with other cardiac atlas-based autosegmentation algorithms published to date.Materials and MethodsOf an atlas of data set from 20 breast cancer patients’ CT scans with recontoured cardiac substructures creation according to the ESTRO guidelines. Performance evaluation on a validation data set consisting of 20 others CT scans acquired in the same treatment position: cardiac substructure were automatically contoured by the Mirada system, using the implemented cardiac atlas, and simultaneously manually contoured by a radiation oncologist. The Dice similarity coefficient was used to evaluate the concordance level between the manual and the automatic segmentations.ResultsDice similarity coefficient value was 0.95 for the whole heart and 0.80 for the four cardiac chambers. Average Dice similarity coefficient value for the left ventricle walls was 0.50, ranging between 0.34 for the apical wall and 0.70 for the lateral wall. Compared to manual contours, autosegmented substructure volumes were significantly smaller, with the exception of the left ventricle. Coronary artery segmentation was unsuccessful. Performances were overall similar to other published cardiac atlas-based autosegmentation algorithms.ConclusionThe evaluated cardiac atlas-based autosegmentation technique, using the Mirada software, demonstrated acceptable performance for cardiac cavities delineation. However, algorithm improvement is still needed in order to develop efficient and trusted cardiac autosegmentation working tools for daily practice.     

深度学习在膀胱癌病理学中的研究进展

膀胱癌的发病率逐年上升,其诊断的金标准依赖于组织病理活检。全载玻片数字化技术可产生大量高分辨率捕获的病理图像,促进了数字病理学的发展。随着人工智能的热潮掀起,深度学习作为人工智能的一种新方法,已经在膀胱癌的肿瘤诊断、分子分型、预测预后和复发等病理图像分析中取得了显著成果。传统病理极度依赖于病理学家的专业水平和经验储备,主观性强且可重复性差。深度学习以其自动提取图像特征的能力,在辅助病理学家进行决策时,可提高诊断效率和可重复性,降低漏诊和误诊率。这不仅能缓解目前面临人才短缺和医疗资源不均的压力,而且也能促进精准医疗的发展。本文就深度学习在膀胱癌病理图像分析中的最新研究进展和前景作一述评。     

人工智能在乳腺癌诊断中应用的研究进展

乳腺癌是世界范围内女性最常见的恶性肿瘤之一,严重威胁女性的身心健康,早期诊断和早期治疗是其良好预后的关键。人工智能(artificial intelligence,AI)是当今科技发展的代表性前沿技术,已在医学影像、病理、辅助决策、医学教育等方面取得了长足的进展,许多AI产品已经从实验阶段过渡到了临床应用阶段。在乳腺癌诊断领域,基于AI的乳腺癌影像技术不仅有望大大减轻临床医生的工作负担,同时也能够不断提高乳腺癌筛查和诊断的准确性及敏感性。本文就当前乳腺癌诊断领域中AI技术的发展及应用现状进行分析和综述,并对乳腺癌影像AI未来的发展方向进行展望,以期为相关AI技术的研究提供参考。     

人工智能在结直肠癌诊治中的应用

近年来结直肠癌的发病率和死亡率逐年上升。结直肠癌的筛查和诊断依赖临床症状、内镜表现、影像学及病理检查,对医师经验有很高要求。手术治疗仍是重要的治疗手段。腹腔镜结直肠癌手术得到了广泛应用,但学习曲线和并发症仍是有待解决的问题。人工智能的发展有望为结直肠癌的防治提供有力的辅助。通过大量数据训练,人工智能在内镜、影像、手术和病理等方面可以模拟人脑进行识别、决策和预测,为医师提供参考,甚至发现新的临床规律。