Smartphone Camera for Angiographic Computer Vision in Vascular Medicine
DOI:
https://doi.org/10.62487/82grqt38Keywords:
TensorFlow, Artery Disease, Smartphone Application, Machine Learning, Computer Vision, Artificial intelligenceAbstract
Aim: This study aimed to develop a TensorFlow Lite algorithm for angiography classification and to deploy it on a basic mobile smartphone device, thereby verifying the proof of concept for creating a comprehensive end-to-end mobile computer vision application for vascular medicine. Materials and Methods: After ethical approval by the local ethics committee, we collected institutional and open source peripheral angiograms of lower limbs. The angiograms were labeled by a researcher with more than 10 years of experience in vascular surgery. The labeling included dividing the angiograms according to their anatomical pattern into the Global Limb Anatomic Staging System (GLASS). The model was developed using the open-source TensorFlow framework for general image classification and deployed as an Android application. Results: The model utilized 700 angiograms, distributed as follows within the femoropoliteal GLASS disease (fp) categories: fp0 – 187 images, fp1 – 136 images, fp2 – 128 images, fp3 – 97 images, fp4 – 152 images. The reference dataset included 372 non-angiographic images (not_angio). Consequently, the entire model included 1,072 images. After training and deployment, the model demonstrated the following performance: a mean accuracy of 0.72. The best self-reported accuracy per class was for fp0 0.72, fp4 0.83 and not_angio 1.0 classes. Conclusion: We discovered that a smartphone camera could be utilized for angiographic computer vision through end-to-end applications accessible to every healthcare professional. However, the predictive abilities of the model are limited and require improvement. The development of a robust angiographic computer vision smartphone application should incorporate an upload function, undergo validation through head-to-head human-machine comparisons, potentially include segmentation, and feature a prospective design with explicit consent for using collected data in the development of AI models.
References
Kim S, Hahn J-O, Youn BD. Detection and Severity Assessment of Peripheral Occlusive Artery Disease via Deep Learning Analysis of Arterial Pulse Waveforms: Proof-of-Concept and Potential Challenges. Front Bioeng Biotechnol. 2020;8:720. doi:10.3389/fbioe.2020.00720. DOI: https://doi.org/10.3389/fbioe.2020.00720
McBane RD, Murphree DH, Liedl D, et al. Artificial intelligence for the evaluation of peripheral artery disease using arterial Doppler waveforms to predict abnormal ankle-brachial index. Vasc Med. 2022;27(4):333-342. doi:10.1177/1358863X221094082. DOI: https://doi.org/10.1177/1358863X221094082
Normahani P, Sounderajah V, Mandic D, Jaffer U. Machine learning-based classification of arterial spectral waveforms for the diagnosis of peripheral artery disease in the context of diabetes: A proof-of-concept study. Vasc Med. 2022;27(5):450-456. doi:10.1177/1358863X221105113. DOI: https://doi.org/10.1177/1358863X221105113
Masoumi Shahrbabak S, Kim S, Youn BD, et al. Peripheral artery disease diagnosis based on deep learning-enabled analysis of non-invasive arterial pulse waveforms. Comput Biol Med. 2024;168:107813. doi:10.1016/j.compbiomed.2023.107813. DOI: https://doi.org/10.1016/j.compbiomed.2023.107813
Kordzadeh A, Askari A, Abbassi OA, Sanoudos N, Mohaghegh V, Shirvani H. Artificial intelligence and duplex ultrasound for detection of carotid artery disease. Vascular. 2023;31(6):1187-1193. doi:10.1177/17085381221107465. DOI: https://doi.org/10.1177/17085381221107465
Rusinovich Y, Rusinovich V, Buhayenka A, et al. Classification of anatomic patterns of peripheral artery disease with automated machine learning (AutoML). Vascular. 2024:17085381241236571. doi:10.1177/17085381241236571. DOI: https://doi.org/10.1177/17085381241236571
Ou C, Qian Y, Chong W, et al. A deep learning-based automatic system for intracranial aneurysms diagnosis on three-dimensional digital subtraction angiographic images. Med Phys. 2022;49(11):7038-7053. doi:10.1002/mp.15846. DOI: https://doi.org/10.1002/mp.15846
Irfan M, Malik KM, Ahmad J, Malik G. StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm. Comput Med Imaging Graph. 2023;108:102271. doi:10.1016/j.compmedimag.2023.102271. DOI: https://doi.org/10.1016/j.compmedimag.2023.102271
Koç U, Sezer EA, Özkaya YA, et al. Elevating healthcare through artificial intelligence: analyzing the abdominal emergencies data set (TR_ABDOMEN_RAD_EMERGENCY) at TEKNOFEST-2022. Eur Radiol. 2023. doi:10.1007/s00330-023-10391-y. DOI: https://doi.org/10.1007/s00330-023-10391-y
Courtman M, Kim D, Wit H, et al. Deep Learning Detection of Aneurysm Clips for Magnetic Resonance Imaging Safety. J Imaging Inform Med. 2024;37(1):72-80. doi:10.1007/s10278-023-00932-8. DOI: https://doi.org/10.1007/s10278-023-00932-8
Wikipedia. Diamond open access. https://en.wikipedia.org/wiki/Diamond_open_access. Accessed March 21, 2024.
TensorFlow. Image classification. https://www.tensorflow.org/lite/models/modify/model_maker/image_classification. Updated 2023. Accessed March 11, 2024.
TensorFlow. Image classification with TensorFlow Lite Model Maker. https://www.tensorflow.org/lite/models/modify/model_maker/image_classification. Updated 2023. Accessed March 11, 2024.
Conte MS, Bradbury AW, Kolh P, et al. Global Vascular Guidelines on the Management of Chronic Limb-Threatening Ischemia. Eur J Vasc Endovasc Surg. 2019;58(1S):S1-S109.e33. doi:10.1016/j.ejvs.2019.05.006. DOI: https://doi.org/10.1016/j.ejvs.2019.05.006
Kaggle. Dataset. https://www.kaggle.com/datasets?tags=13207-Computer+Vision. Accessed 2023.
Github. Tensorflow. Examples. Image classification. Android. https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/android. Accessed March 12, 2024.
Rusinovich Y. Human-centered Evaluation of AI and ML Projects. Web3MLHS. 2024;1(2). doi:10.62487/ypqhkt57. DOI: https://doi.org/10.62487/ypqhkt57
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Yury Rusinovich, Volha Rusinovich, Markus Doss (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.