Intelligent medicine最新文献

Developments in genetics and genomics are progressing at an unprecedented speed. Twenty years ago, the human genome project provided the first glimpses into the human genome sequence and launched a new era of human genetics. The emerging of next-generation sequencing (NGS) in 2005 then made possible comprehensive genetic testing such as exome sequencing and genome sequencing. Meanwhile, great efforts have been put into the optimization of bioinformatic pipelines to make increasingly speedy and accurate variant analyses based on NGS data. These advances in sequencing technologies and analytical methods have revolutionized the diagnostic odyssey of suspected hereditary diseases. More recently, the genotype-phenotype relationship and polygenic risk scores (PRSs) generated from genome-wide association studies have expanded our horizon from rare genetic mutations to a genomic landscape implicated by the combined effect of both rare variants and polymorphisms. At the same time, clinicians and genetic counselors are facing huge challenges conferred by overwhelming genomic knowledge and long sheets of testing reports for comprehensive genomic sequencing. The path toward the “next-generation” clinical genetics and genomics may underlie semiautomatic pipelines assisted by artificial intelligence techniques.

Objective The study aimed to develop a machine learning (ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications (MCs).

Methods We enrolled 463 digital mammographical view images from 260 consecutive patients detected with non-palpable MCs and BI-RADS scored at 4 (training cohort, n = 428; independent testing cohort, n = 35) in the First Affiliated Hospital of Nanjing Medical University between September 2010 and January 2019. Subsequently, 837 textures and 9 shape features were subsequently extracted from each view and finally selected by an XGBoost-embedded recursive feature elimination technique (RFE), followed by four machine learning-based classifiers to build the radiomics signature.

Results Ten radiomic features constituted a malignancy-related signature for breast MCs as logistic regression (LR) and support vector machine (SVM) yielded better positive predictive value (PPV)/sensitivity (SE), 0.904 (95% CI, 0.865–0.949)/0.946 (95% CI, 0.929–0.977) and 0.891 (95% CI, 0.822–0.939)/0.939 (95% CI, 0.907–0.973) respectively, outperforming their negative predictive value (NPV)/specificity (SP) from 10-fold cross-validation (10FCV) of the training cohort. The optimal prognostic model was obtained by SVM with an area under the curve (AUC) of 0.906 (95% CI, 0.834–0.969) and accuracy (ACC) 0.787 (95% CI, 0.680–0.855) from 10FCV against AUC 0.810 (95% CI, 0.760–0.960) and ACC 0.800 from the testing cohort.

Conclusion The proposed radiomics signature dependens on a set of ML-based advanced computational algorithms and is expected to identify pathologically cancerous cases from mammographically undecipherable MCs and thus offer prospective clinical diagnostic guidance.

Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients. Deep learning and its applications in medical imaging, especially in image reconstruction have received considerable attention in the literature in recent years. This study reviews records obtained electronically through the leading scientific databases (Magnetic Resonance Imaging journal, Google Scholar, Scopus, Science Direct, Elsevier, and from other journal publications) searched using three sets of keywords: (1) Deep learning, image reconstruction, medical imaging; (2) Medical imaging, Deep learning, Image reconstruction; (3) Open science, Open imaging data, Open software. The articles reviewed revealed that deep learning-based reconstruction methods improve the quality of reconstructed images qualitatively and quantitatively. However, deep learning techniques are generally computationally expensive, require large amounts of training datasets, lack decent theory to explain why the algorithms work, and have issues of generalization and robustness. The challenge of lack of enough training datasets is currently being addressed by using transfer learning techniques.

Adapting systems and technology for an aging population has benefits for older people, the consumer market industry itself and all of society. To promote knowledge sharing on innovations for healthy ageing and digital inclusion of older people in the Western Pacific Region, a hybrid conference on “Digital inclusion of older people: harnessing digital technologies to promote healthy ageing in the Western Pacific Region” was held on 23 June 2021 by China Academy of Information and Communications Technology, a WHO Collaborating Centre for Digital Health. Barriers from demand side include: (1) unaffordability; (2) poor Information and Communication technology (ICT) knowledge and skills for navigation; and (3) low self-efficacy and motivation. Supply barriers include: (1) youth-centred design; (2) ageism; and (3) anti-facilitative environment including infrastructure and age-biased technology. Existing practices to overcome digital inclusion barriers were shared: (1) landmark initiatives related to the health and social welfare; (2) laws and policies to improve aged care services, strengthen social services, enrich spiritual and cultural life for older people; (3) ICT infrastructure and residential care facilities based on the philosophy of family care and supported by community care; (4) affordable digital application and adaptive feature design to better enable and motivate their desire to use digital technology; and (5) community activities such as trainings and tutorials to enhance digital capacity and literacy of older people. Main principles highlighted include market motivation, human-centered design, creating enabling environments, and multi-stakeholder collaborations to provide guidance to customize strategy under context of different regions and countries, instead of a one-size-fits-all solution, to avoid the risk of exacerbating inequalities experienced by older people, caused by accelerated ICT innovation, and advocate for more affordable products in the silver market.

Medical artificial intelligence (AI) is an important technical asset to support medical supply-side reforms and national development in the big data era. Clinical data from multiple disciplines represent building blocks for the development and application of AI-aided diagnostic and treatment systems based on medical big data. However, the inconsistent quality of these data resources in AI research leads to waste and inefficiencies. Therefore, it is crucial that the field formulates the requirements and content related to data processing as part of the development of intelligent medicine. To promote medical AI research worldwide, the “Belt and Road” International Ophthalmic Artificial Intelligence Research and Development Alliance will establish a series of expert recommendations for data quality in intelligent medicine.

In recent years, the incidence of myopia has increased at an alarming rate among children and adolescents in China. The exploration of an effective prevention and control method for myopia is in urgent need. With the development of information technology in the past decade, artificial intelligence with the Internet of Things technology (AIoT) is characterized by strong computing power, advanced algorithm, continuous monitoring, and accurate prediction of long-term progression. Therefore, big data and artificial intelligence technology have the potential to be applied to data mining of myopia etiology and prediction of myopia occurrence and development. More recently, there has been a growing recognition that myopia study involving AIoT needs to undergo a rigorous evaluation to demonstrate robust results.

Objective Complete and transparent reporting is of critical importance for randomized controlled trials (RCTs). The present study aimed to determine the reporting quality and methodological quality of RCTs for interventions involving artificial intelligence (AI) and their protocols.

Methods We searched MEDLINE (via PubMed), Embase, Web of Science, CBMdisc, Wanfang Data, and CNKI from January 1, 2016, to November 11, 2020, to collect RCTs involving AI. We also extracted the protocol of each included RCT if it could be obtained. CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) statement and Cochrane Collaboration's tool for assessing risk of bias (ROB) were used to evaluate the reporting quality and methodological quality, respectively, and SPIRIT-AI (The Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence) statement was used to evaluate the reporting quality of the protocols. The associations of the reporting rate of CONSORT-AI with the publication year, journal's impact factor (IF), number of authors, sample size, and first author's country were analyzed univariately using Pearson's chi-squared test, or Fisher's exact test if the expected values in any of the cells were below 5. The compliance of the retrieved protocols to SPIRIT-AI was presented descriptively.

Results Overall, 29 RCTs and three protocols were considered eligible. The CONSORT-AI items “title and abstract” and “interpretation of results” were reported by all RCTs, with the items with the lowest reporting rates being “funding” (0), “implementation” (3.5%), and “harms” (3.5%). The risk of bias was high in 13 (44.8%) RCTs and not clear in 15 (51.7%) RCTs. Only one RCT (3.5%) had a low risk of bias. The compliance was not significantly different in terms of the publication year, journal's IF, number of authors, sample size, or first author's country. Ten of the 35 SPIRIT-AI items (funding, participant timeline, allocation concealment mechanism, implementation, data management, auditing, declaration of interests, access to data, informed consent materials and biological specimens) were not reported by any of the three protocols.

Conclusions The reporting and methodological quality of RCTs involving AI need to be improved. Because of the limited availability of protocols, their quality could not be fully judged. Following the CONSORT-AI and SPIRIT-AI statements and with appropriate guidance on the risk of bias when designing and reporting AI-related RCTs can promote standardization and transparency.