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Journal Article Corrected proof Artificial intelligence selection of viable oocytes from phase contrast images for in vitro maturation and fertilization Get access Manoel Veiga, Manoel Veiga Evident Technology Center Europe GmbH, Münster, Germany Search for other works by this author on: Oxford Academic Google Scholar Beatrice Camia, Beatrice Camia Department of Public Health, Forensic and Experimental Medicine, Histology and Embryology Unit, University of Pavia, Pavia, Italy Search for other works by this author on: Oxford Academic Google Scholar Stefan Diepenbrock, Stefan Diepenbrock Evident Technology Center Europe GmbH, Münster, Germany Search for other works by this author on: Oxford Academic Google Scholar CarloAlberto Redi, CarloAlberto Redi Accademia dei Lincei, Roma, Italy Search for other works by this author on: Oxford Academic Google Scholar Andrea Casasco, Andrea Casasco Department of Public Health, Forensic and Experimental Medicine, Histology and Embryology Unit, University of Pavia, Pavia, ItalyCentro Diagnostico Italiano, Milan, Italy Search for other works by this author on: Oxford Academic Google Scholar Manuela Monti Manuela Monti Department of Public Health, Forensic and Experimental Medicine, Histology and Embryology Unit, University of Pavia, Pavia, ItalyResearch Center for Regenerative Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy Correspondence: Department of Public Health, Forensic and Experimental Medicine Histology and Embryology Unit University of Pavia Viale Forlanini 1027100 Pavia, Italy. Tel: +390382987756; E-mail: manuela.monti@unipv.it Search for other works by this author on: Oxford Academic Google Scholar Biology of Reproduction, ioad084, https://doi.org/10.1093/biolre/ioad084 Published: 27 July 2023

Correction to: Modern Pathologyhttps://doi.org/10.1038/s41379-022-01147-y, published online 10 September 2022 The "Competing interests" section was erroneously not transferred from the manuscript to the originally published version of the article. The "Competing interests" section should read: F.Z. is a shareholder of asgen GmbH. P.S. is a member of the supervisory board of asgen GmbH. All other authors declare that they have no conflict of interest. The original article has been corrected accordingly. Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathologyModern PathologyVol. 35Issue 12PreviewArtificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Full-Text PDF Open Access

Microfluidics is used increasingly for engineering and biomedical applications due to recent advances in microfabrication technologies. Visualization of bubbles, tracer particles, and cells in a microfluidic device is important for designing a device and analyzing results. However, with conventional methods, it is difficult to observe the channel geometry and such particles simultaneously. To overcome this limitation, we developed a Darkfield Internal Reflection Illumination (DIRI) system that improved the drawbacks of a conventional darkfield illuminator. This study was performed to investigate its utility in the field of microfluidics. The results showed that the developed system could clearly visualize both microbubbles and the channel wall by utilizing brightfield and DIRI illumination simultaneously. The methodology is useful not only for static phenomena, such as clogging, but also for dynamic phenomena, such as the detection of bubbles flowing in a channel. The system was also applied to simultaneous fluorescence and DIRI imaging. Fluorescent tracer beads and channel walls were observed clearly, which may be an advantage for future microparticle image velocimetry (μPIV) analysis, especially near a wall. Two types of cell stained with different colors, and the channel wall, can be recognized using the combined confocal and DIRI system. Whole-slide imaging was also conducted successfully using this system. The tiling function significantly expands the observing area of microfluidics. The developed system will be useful for a wide variety of engineering and biomedical applications for the growing field of microfluidics.