The development of therapeutics relies on testing their efficiency and specificity in animals and human in vitro models. To optimize the efficiency of a gene therapy, for example, fluorescent reporters expressed by treated cells are often utilized as readouts. Traditionally, the overall fluorescence signal provides an estimate for the global transduction efficiency. However, detailed analysis of the transduction efficiency in individual cells within a tissue remains a challenge. Readout on a single cell level can be realized via fluorescence-activated cell sorting at the cost of tissue dissociation into single cells and loss of spatial information. Complementary, spatial information is accessible via immunofluorescence characterization of fixed samples. However, those approaches impede time-dependent studies and prevent the recording of the dynamic interplay between the viral vector and the target cells in a 3D tissue. Here, we provide a quantitative, three-dimensional characterization of viral transduction efficiencies in living retinal organoids. We combine engineered adeno-associated virus (AAV) vectors, confocal live-imaging, and deep learning-based image segmentation to establish a quantitative test platform for gene delivery. To establish this, we transduced human retinal organoids with specific AAV vectors and imaged the fluorescent reporter expression in 3D. We measured a faster onset (7 days) and higher transduction efficiency (82%) of an AAV vector with optimized serotype (AAV2.NN) compared to two other AAV serotypes (AAV2.7m8, AAV9.NN). This highlights the practicality and functionality of our platform as a testbed for future treatments. The combination of optimized viral vectors, live-imaging, and deep learning-based image processing has the potential to guide the development of therapies in a variety of biomedical applications.