aUniversity of Tennessee Health Science Center, Memphis, Tennessee ... server and communication of the quality assessment (pass or fail) to the camera; ... for low-cost, real-time diagnosis and patient referral in the primary care environment, ...
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A Network Infrastructure for Automated Diagnosis of Diabetic Retinopathy Yaqin THE
a Li ,
b Karnowski ,
b Tobin ,
Ph.D., Thomas P. Kenneth W. Luca Giancardob, Edward Chauma, M.D., Ph.D.
UNIVERSITY of TENNESSEE HEALTH SCIENCE CENTER
Ph.D.,
aUniversity
of Tennessee Health Science Center, Memphis, Tennessee bOak Ridge National Laboratory , Oak Ridge, Tennessee
Memphis, Tennessee
Purpose
Automatic Diagnosis: After the images are submitted to the server, they are first subject to a quality assessment (QA) to ensure sufficiency for further
The aim of our work is to build an automated system for detection and diagnosis of diabetic retinopathy disease in real time from digital images taken in a primary care setting. Diabetic retinopathy (DR) is the leading cause of blindness in the industrialized world today, yet treatments can preserve vision in patients with DR if the disease is diagnosed at an early stage. The medical community should be screening 400,000 patients for diabetic eye disease every week in the United States alone. It is anticipated that by 2025 the number of patients requiring screening will exceed 1 million per day, worldwide. In our web-based network, retinal images from diabetic patients are transmitted from fundus cameras in DICOM format using secure protocols to our diagnostic server. The retinal images are then graded to stratify disease level, recommend a management plan, and generate a report accessible to the client. In the current stage, with the automatic diagnostic engine still under development, the system runs in a semi-automated fashion and disease stratification is carried out manually by an ophthalmologist. Eventually the system will provide fully automation of real-time DR diagnosis when the computer aided diagnostic engine is installed. In this work, a network infrastructure is described for the fully automated diagnosis system and evaluated in a primary care setting.
analysis. The image that passed QA will have its anatomy structure analyzed, features extracted, lesions segmented and a diagnosis assigned according to the posterior probability, P(wi|v), of each defined disease state wi using a content-based image retrieval (CBIR) method that evaluates the retrieval response. The full automated diagnostic engine is under development and has not been incorporated into the current system yet. In the current stage, the diagnosis is performed in a semi-automated fashion, with disease stratified by an ophthalmologist and the management plan generated automatically by the server.
Confirmation and Report: The auto-diagnosis result along with the images and patient meta data are reviewed by an ophthalmologist for confirmation. After review is complete, the ophthalmologist signs and encrypts the report using an X.509 certificate, and then sends the confirmed report with digital signature and encryption to the referring physician in the participating tele-ophthalmology clinic.
Data transmission protocols: Secure data transmission: To provide data security for communications over the Internet, and to meet HIPAA compliance requirements, all data transmission between the client and the server is performed using a cryptographic protocol. Secure File Transfer protocol (SFTP): to transfer images to the server and communication of the quality assessment (pass or fail) to the camera;
Methods
HTTP over Secure Socket Layer (HTTPS): to generate and access the report on the server for authorized users.
Overview:
Data encryption: Encrypted PDF report is generated to provide
To implement an automated diagnosis system, it is crucial to design the underlying network infrastructure to provide high speed data transmission for real time image analysis, secure data transmission, and encryption of patient sensitive information to meet the HIPAAcompliance requirements, and also to address issues of instable network connectivity.
Robustness: Robustness aspects in the network design include To ensure robust data transmission in instable network connectivity, the client closely monitors the network status, and reacts in a real-time manner; To guarantee no loss of data, a sliding window algorithm is implemented in the duplex data transmission.
In our design, a client-server model approach is implemented for the data transmission between the client and the diagnostic server. The clients are the participating tele-ophthalmology clinics in a regional network. The geographic distribution of the clinics is shown in Fig. 1. After the fundus images of both eyes are captured at the teleophthalmology clinic, they are exported and submitted to the server for an automated quality assessment (QA) to assure adequacy for diagnosis, and the QA results are communicated to the end-user in real time. Inside the server, the automatic diagnostic service will retrieve images and patient metadata, perform processing task to identify anatomical structure location, extract features, detect lesions, and assign an diagnosis with appropriate management plan to generate a report accessible to the referring physicians. An ophthalmologist then reviews the images and the report, and provides validation before the confirmed report is prepared for the end user. An ophthalmologist can also override the computer aided auto-diagnosis by manually retrieving patient information and assigning a diagnosis through the web interface to generate a confirmed report.
Data integrity and encryption using an X.509 certificate to ensure security of patient sensitive information; Password protected access for referring physicians as an email attachment.
Results Performance evaluation:
GUI design for work flow:
Real time availability is one of the key features of the telemedical system and it is evaluated by the average total response time. From Table 1, the estimated average total response time is less than 2 minutes.
Client Application GUI
Table 1. System response time in a real clinic setting
Figure 1. Geographic map of participating clinics
System Requirement:
Table 1. System response time in a real clinic setting
Secure data transmission between the clinic and the server; Encryption of patient sensitive information for HIPAA compliance; Robustness in case of instable network connectivity; Real time analysis.
Server web GUI Sample report
Statistics from the current network since its inception in mid-February is shown in Fig. 4.
System Architecture:
Figure 3. GUI design for the teleophthalmology Network
Figure 4. Statistics of the current running system
Conclusions In our work, a network infrastructure for automated diagnosis of diabetic retinopathy is designed, implemented and evaluated. This automated network provides a method for low-cost, real-time diagnosis and patient referral in the primary care environment, providing access to expert diagnosis for underserved patients, and high-throughput methods to meet the growing need for screening in rapidly expanding at-risk populations. With the network infrastructure in place, our next step is to fully incorporate computer aided lesion detection methods for automatic diagnosis of diabetic retinopathy.
Acknowledgements studies were supported in part by grants
Figure 2. HIPAA Compliant telemedical network infrastructure
Work Flow: Tele-Opthalmology Clinic: The tele-opthalmology clinics are regional primary care clinics. In our tele-medical system, the clinic acquires fundus images of the retinal using VisuCam Pro NM camera from Carl Zeiss Meditec. The mages are then submitted to a dedicated diagnostic server in DICOM data format, along with patient meta data entries.
These from Oak Ridge National Laboratory, the National Eye Institute, (EY01765), the United These studies were supported in part by grants from Oak Ridge States Army Medical and Material Command, Telemedicine and National Laboratory, the National Eye Institute, (EY01765), the United Advanced Technology Research Center (W81XWH-05-1-0409), by an States Army Medical and Material Command, Telemedicine and unrestricted UTHSC Departmental grant from Research to Prevent Advanced Technology Research Center (W81XWH-05-1-0409), by an Blindness, New York, NY, and by The Plough Foundation, Memphis, unrestricted UTHSC Departmental grant from Research to Prevent TN. Blindness, New York, NY, and by The Plough Foundation, Memphis, TN.
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Research to Prevent Blindness
[7] T. P. Karnowski, V. P. Govindasamy, K. W. Tobin, E. Chaum, "Locating the Optic Nerve in Retinal Images: Comparing Model-Based and Bayesian Decision Methods," in The 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Aug. 30--Sept. 3, 2006, New York City, USA, pp. 4436--4439.
