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. 2021;39(3-4):677-700.
doi: 10.1007/s00354-021-00131-5. Epub 2021 Jun 27.

Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence

Amirhossein Peyvandi  1 , Babak Majidi  1   2 , Soodeh Peyvandi  3 , Jagdish Patra  4
Affiliations

Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence

Amirhossein Peyvandi et al. New Gener Comput. 2021.

Abstract

The COVID-19 pandemic resulted in a significant increase in the workload for the emergency systems and healthcare providers all around the world. The emergency systems are dealing with large number of patients in various stages of deteriorating conditions which require significant medical expertise for accurate and rapid diagnosis and treatment. This issue will become more prominent in places with lack of medical experts and state-of-the-art clinical equipment, especially in developing countries. The machine intelligence aided medical diagnosis systems can provide rapid, dependable, autonomous, and low-cost solutions for medical diagnosis in emergency conditions. In this paper, a privacy-preserving computer-aided diagnosis (CAD) framework, called Decentralized deep Emergency response Intelligence (D-EI), which provides secure machine learning based medical diagnosis on the cloud is proposed. The proposed framework provides a blockchain based decentralized machine learning solution to aid the health providers with medical diagnosis in emergency conditions. The D-EI uses blockchain smart contracts to train the CAD machine learning models using all the data on the medical cloud while preserving the privacy of patients' records. Using the proposed framework, the data of each patient helps to increase the overall accuracy of the CAD model by balancing the diagnosis datasets with minority classes and special cases. As a case study, the D-EI is demonstrated as a solution for COVID-19 diagnosis. The D-EI framework can help in pandemic management by providing rapid and accurate diagnosis in overwhelming medical workload conditions.

Keywords: Blockchain; COVID-19 pandemic; Computer aided diagnosis; Emergency response; Machine learning.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Decentralized framework for sharing data between different data holders for telemedicine
Fig. 2
Fig. 2
Deployment and test phases of D-EI
Fig. 3
Fig. 3
A patient viewing his/her COVID-19 test results added to the system
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Fig. 4
The architecture of the deep neural network of Arrhythmia classification model
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Fig. 5
The architecture of the deep neural network of COVID-19 cough classification model
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Fig. 6
The architecture of deep neural network of X-ray classification model
Fig. 7
Fig. 7
The architecture of U-Net for chest CT segmentation
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Fig. 8
The overall fusion method used in this paper
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Fig. 9
A numerical example of the fusion method used in this paper
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Fig. 10
A sample of the CT scan dataset
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Fig. 11
Predicted infection mask
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Fig. 12
D-EI framework for sharing data between different data holders for telemedicine
Fig. 13
Fig. 13
Sequence diagram of the proposed framework for COVID CAD model training
See this image and copyright information in PMC

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