Application of artificial intelligence in 3D printing physical organ models

Abstract

Artificial intelligence (AI) and 3D printing will become technologies that profoundly impact humanity. 3D printing of patient-specific organ models is expected to replace animal carcasses, providing scenarios that simulate the surgical environment for preoperative training and educating patients to propose effective solutions. Due to the complexity of 3D printing manufacturing, it is still used on a small scale in clinical practice, and there are problems such as the low resolution of obtaining MRI/CT images, long consumption time, and insufficient realism. AI has been effectively used in 3D printing as a powerful problem-solving tool. This paper introduces 3D printed organ models, focusing on the idea of AI application in 3D printed manufacturing of organ models. Finally, the potential application of AI to 3D-printed organ models is discussed. Based on the synergy between AI and 3D printing that will benefit organ model manufacturing and facilitate clinical preoperative training in the medical field, the use of AI in 3D-printed organ model making is expected to become a reality.

Keywords: 3D printing; Artificial intelligence; Organ models; Preoperative training.

Graphical abstract

Fig. 1

The relationship between AI, ML, DL, and the classification of ML applications in 3D printing and the neural networks used by DL. Modified with permission from Ref. [24]. Copyright 2021 IOPscience.

Fig. 2

Schematic diagram of the different types of direct and indirect printing and direct printing. (A) Direct 3D printing of organ models, 3D printing of casting molds, and 3D printing of sacrificial material fabrication models. (B)b₁ Stereo lithography Appearance (SLA)、b₂ Fused Deposition Molding (FDM) 、b₃ Inkjet printing、b₄ Digital Light Processing (DLP). Reproduced with permission from Ref. [13]. Copyright 2021 Wiley.

Fig. 3

The process of 3D printing organ models. Take 3D printing heart models as an example. Reproduced with permission from Ref. [27]. Copyright 2021 Elsevier.

Fig. 4

Example of the ML application in 3D printing. (A) An example of supervised learning is where the training model is trained to predict the print suitability of the ink. (B) An example of super-resolution unsupervised learning is where the model learns from high-resolution images. Low-resolution input data can be generated automatically without laborious manual annotation. (C) Parameter-optimized reinforcement learning tasks, where desired outcomes, such as resolution and manufacturing speed, can be used as rewards for training the model. (D) Example methods for deep learning.

Fig. 5

The three stages of 3D printing organ models and the factors to consider at each stage.

Fig. 6

The three stages of 3D printing organ models and the factors to consider at each stage. (A) Image acquisition and processing. Super-resolution is achieved using unsupervised learning, where the input low-resolution (LR) image is converted to a high-resolution (HR) image after layers. (B) Image Segmentation. Segmentation of high-resolution 2D images is performed to separate the desired 3D tissue by applying a series of thresholds to obtain a 3D surface model. Modified with permission from Ref. [60]. Copyright 2018 Elsevier.

Fig. 7

Example of a 3D printed organ model applying a neural network. (A) The printing parameters are fed into the neural network as training data to predict the filament diameter and the mechanical properties of the material. (B) Development of flowcharts for ink. Modified with permission from Ref. [94]. Copyright 2022 MDPI.

Fig. 8

Example of Closed-Loop AI Printing. (A) Closed-loop voltage control framework for the Liquid Metal Jet Printing process. Reproduced with permission from Ref. [107]. Copyright 2018 Elsevier. (B) Workflow of a closed-loop feedback control algorithm based on a convolutional neural network (CNN) model. Modified with permission from Ref. [108]. Copyright 2019 Elsevier.

Fig. 9

Future perspectives on the use of AI for 3D printing of organ models. Modified with permission from Ref. [24]. Copyright 2021 PubMed Central.