
doi: 10.1002/smm2.70016
ABSTRACTOrganoids are tissue analogues formed through in vitro three‐dimensional culture of stem cells, possessing specific spatial structures. Organoids have become integral to various biomedical fields, including disease pathogenesis, model construction, regenerative and precision medicine, drug screening, tissue and organ development, toxicology, and pathological analysis. However, the diversity of organoid types and variations in their production processes have led to inconsistencies in their application for assessment and analysis. To date, no comprehensive standards or guidelines for evaluating organoids have been established. Artificial intelligence (AI) technology is extensively employed in biomedical image analysis, data processing, and molecular structure prediction, demonstrating benefits in the assessment of organoids. This review will examine the application of AI across various aspects of organoid assessment, such as omics, histology, morphology, functional properties, and drug screening, with the goal of offering new perspectives on organoid assessment.
assessment, TA401-492, drug screening, artificial intelligence, single‐cell sequencing, Materials of engineering and construction. Mechanics of materials, organoids
assessment, TA401-492, drug screening, artificial intelligence, single‐cell sequencing, Materials of engineering and construction. Mechanics of materials, organoids
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 3 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
