Modeling adult skeletal stem cell response to laser-machined topographies through deep learning

Bone Laser Patterning to Decipher Cells Organization

“…Some work was conducted using deep learning to optimize the development of laser machining, predicting the outcome of skeletal stem cells arrangement according to the laser-machined pattern [37]. The rise of AI-based technologies could significantly improve future research, especially in finding optimum parameters regarding patterning design and biological consequences.…”

The future of bone regeneration: integrating AI into tissue engineering

“…To stick with the previous comparison, it could state what defines a dog without needing DNA or learning zoology. Once adapted for stem cell biology, AI was able to predict the statistically likely skeletal stem cell response to an unseen surface topography, using only 203 fluorescent images of live stained cells [35]. More importantly, using the AI predictions led to an experimentally validated topographical parameter for inducing cell alignment, without a single piece of cellular, chemical or physical biology encoded.…”

“…If this study was advanced to HBMSCs, with single-cell and colony behaviour investigated, it is highly likely that a DNN-based model could generate predictions for stem cell behavioural response to nanotopographies, relevant for implant and scaffold design. Combined with prior microscale studies [35], a dual-network system, a deep convolutional generative cooperative network rather than an adversarial network, where one network designs topographies and another predicts the cell response, has the potential for a completely AI-designed topographical patterning for optimal proliferation / differentiation, patient and injury specific. By splitting the workload between networks, less GPU power is required and there is a greater level of interpretability and accuracy, as the network responsible for cell response prediction, the model, can be independently tested, validated, and continuously improved, while the cooperative network, the generator, can design topographies faster than human capability.…”

“…Characteristics of biomaterials are a combination of both the biophysical (including biomechanical) and biochemical features, resulting in a wide range of biomaterial properties, many of which have been detailed by Gaharwar et al, including improved proliferation and targeted differentiation [16]. Through manipulation of these characteristics via a wide range of manufacturing techniques, from additive manufacturing processes such as 3D printing to ablation processes such as 2D laser femtosecond laser machining, the behaviour from a single cell to a whole colony can be modulated, resulting in cell-response altering (cell-instructive) biomaterials [34,35]. Biophysical cues can result from a wide range of features, including the stiffness, structure and, importantly, topography of the biomaterial.…”

“…Medical imaging is often at the forefront in integrating AI, as it is a field of complex and ambiguous data, which often requires lengthy manual processing, where an expert will medically interpret and analyse, and then annotate, large amounts of data [91,92]. While ConvNets and deep learning have traditionally been utilised effectively in segmentation of tissues and organs, increasingly more complex tasks are being advanced through deep learning, such as detection of abnormalities or non-invasive cell counting, morphological identification and behavioural prediction, including with stem cells [35,87,[93][94][95][96][97][98][99].…”