Figure 4: An example of a Deep Neural Network (DNN) with three different input artificial neurons, two hidden processing layers with each four artificial neurons and one output layer.

The magnitude of input, mathematical computations, speed of analysing and quality of outputs depends on the capacity of the hardware available, the architecture and training or conditioning of the ANN, and the infrastructural environment allowing an undisturbed computation or deep learning process.

Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input.

The magnitude and quality of raw data depends on the operational size or economy of scale and the existence of a well-operated quality system management of the vein-to-vein process in all its complexity of primary, supportive, and steering processes.

Deep neural networks

To build and condition such artificial DNN a meticulous and detailed analysis of all three categories of processes – primary, supportive, and steering, and their interrelations and interconnections is essential. Conditioning artificial intelligence and the needed DNN is the hardest part because you need

1. a large data set (big data).
2. a large amount of computational capacity and power (quantum computer).
3. to condition the DNN to provide 100% correct quality outcomes.

To condition AI and its DNN one needs to give the inputs from the existing data set and compare the output or outcome with the known historic outcome of the used data set. Since at onset AI and DNN are not yet conditioned, the outcome will be wrong. Going through the whole data set you may create a function that shows how the computed outputs differ from the real outputs, a so-called Cost Function. Ideally the Cost Function should be zero. This can be achieved by adjusting the so-called weights of the neurons which could efficiently be done using a technique called Gradient Descent. This works by changing the weights in small increments after each data set iteration by computing the derivative or gradient of the Cost Function to see in which direction the minimum will be. This is a time-consuming process that needs a large computational power, where fortunately the changing of the weights is done through Gradient Descent in an automated way using quantum computers [10].

Machine learning and robotics

Machine learning refers to the ability of a machine, usually a computer, to learn using large data sets (big data) instead of using hard coded rules. This type of learning takes great advantage of the processing power and speed of the modern computer generation, able to process easily and fast large data sets (big data). The learning can be done in a supervised (using labelled data sets) or unsupervised way (using data sets with no specified structure). In the blood supply we generally use specified data sets, and therefore should apply supervised machine learning.

In the advanced world machine learning has been introduced in the clinical decision-making for transfusion, using data sets and stochastic dynamic programming [10] to predict the need [12-16]. ANNs have been shown to provide an acceptably reliable mechanism to evaluate preoperatively the potential for peri-operative transfusion across a wide range of surgical interventions, reducing unnecessary cross-matching and out of stock of blood components for a considerable amount of time during the day [15].

Pretransfusion compatibility testing is also entering AI through machine learning, with the introduction in the late 20st Century of computerized or electronic crossmatching, saving time, reagents and labour [17,18]. This is considered as safe as the immediate spin crossmatch provided there are no clinically significant alloantibodies present and there is no ABO discrepancy.

Increasingly, robotics has been introduced in almost all manufacturing processes of blood, e.g., automated mixing/weighing machines in blood collection, blood grouping and infectious disease marker testing robots using algorithms, advanced blood cell separators and centrifuges, computerized labelling machines and machine learning-based cold chain management.

Digital Foot-Printing and the Future of Transfusion Medicine

With the rapid development and extension of the use of AI with its Machine Learning and Deep Learning capacities (Figure 5), the approach might help accelerate the bridging of the global gaps existing in the blood supply and clinical use between the more advanced world and the developing world [19].

Over the past decades the most significant change has been the acceptance of Transfusion Medicine (TM) as a medical subdiscipline focusing on the patient to determine appropriate therapy. In the early years of this century greater emphasis was placed on patient blood management (PBM) [20,21], haemovigilance [22,23], cost recovery and integration in the healthcare system [24]. During this time, TM refocused on the patient through restrictive therapeutic guidelines to ensure that every blood transfusion is evidence-based and that alternative therapies are considered to reduce risk to the patient. As a global result, blood usage, especially red cell transfusion, has decreased over the last two decades, further effected by the current pandemic.

While integrated healthcare delivery evolves, efficient suppliers, including blood establishments, must become LEAN [24] and practice continuous improvement. The prime goal is efficiency embraced stewardship. A blood availability and safety digital footprint driving efficiency, safety, patient satisfaction and lower cost might be the road to the new Rome and contribute to achieving several of the 2016-2030 UN Sustainable Development Goals (SDG) (4) as well as the 2030 UN Universal Health Coverage (UHC) goal (26).