Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Figure 1. Search strategy showing inclusion and exclusion criteria.

Figure 2. Classification of articles by (A) imaging modality. (B) AI techniques. MA, manual; SA, statistical analysis; ML, machine learning; DL, deep learning; RL, reinforcement learning.

Figure 3. Stages of plaque formation and rupture. (A) Healthy artery, (B) LDL penetration via intima layer, (C) dislodging of SMC, and (D) wall rupture (courtesy of AtheroPoint, Roseville, CA, USA). SMC, smooth muscle cell.

Figure 4. Pathological images were depicting different components of plaque. (A) Healthy wall, (B) neovessels, (C) calcified plaque, and (D) intraplaque hemorrhage (courtesy of Dr. Luca Saba, U of Cagliari, Italy).

Figure 5. Imagining machines of (A) MRI, (B) CT, and (C) carotid ultrasound scanning using a linear probe (MRI/CT images-courtesy of Dr. Luca Saba, Italy, and US image, courtesy of AtheroPoint, CA, USA). MRI, magnetic resonance imaging; CT, computed tomography.

Figure 6. Carotid plaque scans from (A) MRI scans of ICAP; the yellow arrow represents intraplaque hemorrhage (A1) hyperintense on T1-wt MRI, (A2) hypointense on TOF, (A3) hyperintense on T1-wt MRI after contrast medium injection in sagittal, and (A4) axial view, (A5, A6); the red area represents the intraplaque hemorrhage in the axial section of T1-wt MRI. (B) [courtesy of (82)], (B) CT scans of the ICAP; the white arrow represents the plaque developed (B1) axial, and (B2) sagittal scan [courtesy of (83)]. (B) US scans of ICAP, (C1, C2) represent the symptomatic plaque images, (C3, C4) corresponding color Doppler images, and (C5, C6) delineated plaques (40) (courtesy of AtheroPoint, Roseville, CA, USA) [Permitted to reproduce Figure (A) and (B)]. MRI, magnetic resonance imaging; ICAP, internal carotid artery plaque.

Figure 7. Comparison of the IPH visualization in (A) CTA, (B) MRA, and (C) the histopathology section; the white arrow represents IPH [source (84), Permitted to reproduce]. IPH, intraplaque hemorrhage; CTA, computed tomography angiography; MRA, magnetic resonance angiography.

Figure 8. Generalized global ML architecture for tissue classification using MRI, CT, and US (courtesy of AtheroPoint, Roseville, CA, USA). GS, gold standard; PE, performance evaluation; AUC, area under the curve; SE, sensitivity; SP, specificity; DOR, diagnostic odds ratio; MRI, magnetic resonance imaging; CT, computed tomography.

Figure 9. Local assessment of plaque echo-morphology, in terms of (A,B,C) hypoechogenicity and (D) heterogeneity [Source (33), Permitted for reproduction].

Figure 10. The DL System architectures of (A) The global architecture, (B) U-Net, (C) CNN, and (D) LSTM memory cell (courtesy of AtheroPoint, Roseville, CA, USA). DL, deep learning; CNN, convolutional neural network; LSTM, long short-term memory.

Figure 11. A visual example of DL-based CNN classification results shown in color representing different components of plaque [source: (169), Permitted for reproduction]. CNN, convolutional neural network; DL, deep learning.

Figure 12. The global architecture of the TL models (courtesy of AtheroPoint, Roseville, CA, USA). TL, transfer learning.