A bibliometric analysis of primary ovarian insufficiency from 2010 to 2020.

OBJECTIVE: This study aimed to carry out a bibliometric analysis of primary ovarian insufficiency (POI) from 2010 to 2020 and to reveal the research status and hotspots in the future. METHOD: A total of 3087 articles and reviews related to POI published from 2010 to 2020 retrieved from the Web of Science Core Collection were used for bibliometric analysis. CiteSpace and VOSviewer were adopted to analyze countries and regions, organizations, authors, journals, keywords and co-cited references. RESULTS: The number of publications about POI increased year by year. The USA produced the largest number of publications and the most influence in this field. The main research directions of POI can be roughly divided into four aspects according to the analysis of keywords and co-cited references: genetic research of POI; stem cell therapy for patients with POI; prediction of ovarian function; and fertility preservation of cancer patients. Genetic research and stem cell therapy may become research hotspots in the future. CONCLUSION: This study might be the first bibliometric study to analyze publications of POI from multiple indicators, in order to provide new opinions for the research trends and possible hotspots of POI.

Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence.

BACKGROUND: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients' treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. METHODS: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. RESULTS: Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. CONCLUSIONS: This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.