By Maulik M Patel

Organ transplantation became and has been an unyielding beacon of hope for patients with potentially deadly illnesses. Nevertheless, there is still the issue of a shortage of organs as there are millions of people who are waiting for organs globally. The WHO also reported that there is a shortage of organ transplantation worldwide 120,000 people in the United States are waiting for an organ transplant and only around forty thousand transplants are performed annually.

Among the most viable ways, though, lies the bioprinting of human tissues. Bioprinting is an additive manufacturing process, wherein successive layers of cell-laden bioinks are used to form biological tissues or organs. What is revolutionary about this technology though is the recent addition of AI that is making major alterations from tissue selection for printing with a typical tissue printing method referred to as tissue by the ink.

The Organ Shortage Crisis: In Numbers We Trust

These figures present the true picture of organ transplantation. Unfortunately, statistics from the United Network for Organ Sharing (UNOS) reveal that 17 people die everyday waiting for a transplant in the US alone and the situation is worse globally with millions of patients unable to access these organs.

It applies to all the organs of the principal magnitude. For instance, kidney failure majorly impacts 850 million people globally, and of them, hundreds of thousands require transplants annually. Similar to liver transplants, there is a comparable level of need but less than even 10% of those requiring it can go through with the procedure. They have discovered that this is insufficient, and thus regenerative medicine has become the new paradigm due to the exciting use of AI in bioprinting.

Enter Bioprinting: Tales of Hope

They are recent technologies developed from 3D printing that use bio-inks comprising living cells to create tissues. The very first major development of bioprinting took place in 2002 when the miniature kidney was printed. After that day, scientists could print simple tissues, skin grafts, cartilage, and liver tissue. Finally, the aim is to get into organ tissue engineering to construct organs like the heart, kidneys, and so on, which could be grafted into the human body.

One advantage of bioprinting is that it may easily be possible to create tissues using the patient’s cells in such a way that the tissue wouldn’t get rejected. However, the mentioned procedure is not without difficulties. Organ generation has a tremendous challenge in terms of the human anatomy including hierarchical organization, blood supply and tissue physiologic competency of the various human organs.

AI’s Role in Advancing Bioprinting

It is also worth stating that the combination of AI in the process of bioprinting is beneficial. AI’s ability to analyze massive datasets, predict outcomes, and optimize processes is invaluable when it comes to creating living tissues. Here are some of the key ways AI is revolutionizing this technology:

Material Optimization: AI has become integral to optimizing biomaterials used in bioprinting, significantly streamlining the research process. By analyzing vast datasets from previous experiments, AI helps researchers quickly identify the most suitable bio-inks for various tissue types. This reduces the time spent on trial and error, potentially saving years of research. For instance, deep learning algorithms are employed to study experimental data and suggest the best biomaterial compositions. Probabilistic models such as Bayesian optimization are also used to predict the outcome of material combinations, ensuring that the right bio-ink is chosen for each specific application. A practical example of this is the use of convolutional neural networks (CNNs) to predict the effectiveness of different hydrogels in tissue scaffolding, allowing researchers to focus on the most promising candidates.

Precision and Customization: AI significantly enhances the precision of the bioprinting process by enabling the creation of customized tissue structures tailored to individual patients. By utilizing data from imaging techniques like CT or MRI scans, AI can generate highly accurate 3D models that guide the printing of tissues. Generative adversarial networks (GANs), for instance, can create detailed models from imaging data, ensuring that the printed tissues are precise replicas of the patient’s biological structures. Additionally, reinforcement learning models optimize the bioprinting parameters to match the unique properties of the patient’s tissue, guaranteeing that the printed material mimics the exact cellular and anatomical characteristics. A relevant application of this technology is in the creation of personalized heart valve models, where AI-generated 3D structures are used to print highly accurate valves specific to each patient’s anatomy.

Process Automation: AI is a key driver in the automation of bioprinting processes, reducing the risk of human error and ensuring consistency in the production of tissues. Through robotic process automation (RPA), AI takes over repetitive tasks, such as cell placement and layer alignment, making the process more efficient. Computer vision further enhances this by monitoring the process in real time, ensuring that every layer of printed tissue is properly aligned and detecting any irregularities that could compromise the quality of the final product. Automated bioprinting systems, driven by AI, continuously adjust printing parameters based on real-time feedback, ensuring that structures like skin grafts are printed with precision, ultimately improving outcomes for patients in need of tissue transplants.

Simulation and Testing: One of the most critical applications of AI in bioprinting is in the simulation and testing of tissues before they are transplanted into the body. AI models can predict how bioprinted tissues will behave under biological conditions, reducing the reliance on animal models and early human trials. Using finite element analysis (FEA), for example, researchers can simulate the mechanical behavior of tissues, such as how they will respond to forces like stress and strain in the body. Predictive machine learning models go even further by simulating how these tissues will interact with the body’s immune system, helping to identify potential issues such as tissue rejection. Recurrent neural networks (RNNs) are used to model the long-term viability of bioprinted tissues, such as liver cells, predicting their functionality over time and reducing the need for extensive experimental testing.

Accelerating Vascularization: One of the most complex challenges in bioprinting is creating vascular networks within printed tissues, which are essential for delivering nutrients and oxygen. AI has made significant strides in this area, helping researchers design and optimize these networks. Through evolutionary algorithms, AI assesses biological data like nutrient diffusion rates and blood flow requirements to determine the best routes for capillaries within bioprinted tissues. Neural networks also aid in this process by predicting the optimal configuration for these vascular networks based on tissue geometry and physiological data. A real-world application of this is the use of deep reinforcement learning to optimize vascular structures in bioprinted kidneys, ensuring that the tissue remains functional by maintaining adequate oxygen and nutrient supply.

Real-World Impact: AI’s impact on bioprinting is now extending beyond theoretical research, with real-world applications beginning to take shape. Natural language processing (NLP) models analyze scientific literature and patient data to recommend new bioprinting techniques or personalized treatment plans. Generative models, often used to create innovative biofabrication methods, are unlocking new possibilities that traditional techniques couldn’t achieve. One notable example is the use of AI in the development of bioprinted skin for burn victims, where AI tools analyze patient data to create functional, personalized tissue that mimics the pigmentation, thickness, and texture of the patient’s original skin, improving the chances of successful grafting and healing.

Three years ago in 2019, a research team from Israel was able to complete 3D printing of a small yet fully connected human heart. However, this heart was only a few millimeters in size and not viable for transplantation, but it was a big step forward towards the creation of bio-printed organs. Of late, there is Organovo for example a pioneer in bioprinting that has been able to develop liver tissue which is already in the market for drug screening.

The Future of Organ Transplants: AI-Driven Innovation

The current involvement of AI in bioprinting will likely expand drastically as computation ability is enhanced and data is accumulated. Shortly, AI will likely not only improve bioprinting as a process, but also determine the chances of transplant success, monitor the patient’s recovery, and aid in post-transplantation management. Analyzing intricate biological patterns, AI opens the door to producing organs that are capable of healing themselves or repairing the tissues that are damaged.

According to a 2021 report by Grand View Research, the worldwide 3D bioprinting market share will rise from $1. US dollars in 2020 to $4. AI is expected to be worth around $4 billion by 2028, owing to market growth. This expansion will help to reduce the world’s organ scarcity, giving hope to people in need of transplant surgeries.

AI is not just a bioprinting facilitator. A fundamental tenet of the next development in tissue engineering and regenerative medicine. AI is rapidly turning the bioprinting of human tissues from fiction to reality due to its efficiency in the selection of materials, sharpness, and speed. There are major obstacles on the horizon but for the union of AI with bioprinting, the opportunities are enormous: they can overcome the lack of donor organs and give a new life to many people.

(The author is a seasoned expert in Biotech and Artificial Intelligence)