Automating the Diagnosis, Stratification, and Management of DR

This network and method addresses the needs of underserved populations and represents a novel asset to broad-based screening of DR and potentially other diseases of the retina. The ultimate aim of our work is to develop, validate, and implement a fully automated system for the detection and diagnosis of DR in real time from digital images in statistically significant and quantifiable terms. In our web-based network, retinal images from diabetic patients are transmitted from fundus cameras in DICOM format using secure protocols to a 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 by an ophthalmologist. Eventually, the system will provide a fully automated real-time diagnosis of DR and other retinal diseases.

14.1. Network Infrastructure

The ubiquitous connectivity of the Internet provides an effective way of exchanging medical information and delivering services over a large geographic region, and the widespread availability of network bandwidth provides sufficiently high throughput for real-time data transmission. The advancement of technology makes remote delivery of consultative health care feasible and cost-effective. Telemedical network care has been applied in a variety of medical areas including home monitoring, emergency care, psychiatry, and health care in penal systems, to name just a few. However, most telemedicine systems are more focused on data storage and retrieval, such as teleconferences and televideo transmissions.22 They have not tapped into the potential of computing power and image analysis for data processing. The ever-increasing computing capabilities of electronic devices open a new horizon for computer-aided and mobile decision-making and automated data management.

Ocular telehealth using digital images has proven to be an accurate and reliable method of diagnosing DR, which demonstrates that telehealth is a valid solution for the delivery of ophthalmic care. Our goal is to build a telemedical network for the automated diagnosis and management of DR in the primary care space. To achieve the goal of automating the

421

Thomas P. Karnowski et al.

detection and diagnosis of diabetic eye disease in real time from digital images taken in a primary care setting, a novel network infrastructure is established.

In our ocular telehealth network, the participating primary care clinics are connected to a diagnostic computer server. The web-based telemedical network adds portability and significant flexibility in health care delivery, providing access to expert diagnosis and high throughput potential to meet the growing need for disease assessment and management in rapidly expanding at-risk and underserved patient populations.

14.1.1. System Requirements

To implement an automated diagnosis system, it is necessary to design the underlying network infrastructure to meet the following requirements:

•Secure data transmission between the clinics and the server,

•Encrypted patient-sensitive information to meet the HIPAA-compliance requirements,

•Robust data transmission to guarantee that there is no loss of data and to address issues of unstable network connectivity,

•Data reliability and integrity, and

•High-speed data transmission for high throughput image analysis.

14.1.2. Network Architecture Design

In the network design for ocular telehealth, a client-server model approach is implemented for the data transmission between the client and the diagnostic server (Fig. 14.1).23 After the fundus images of both eyes are captured by a commercial retina camera, they are exported and submitted to the server for an automated quality assessment (QA) metric to assure adequacy for the purpose of diagnosis. The QA results are communicated to the end user in real time. Inside the server, the automatic diagnostic service retrieves the images and patient metadata, perform an image-processing task to identify the anatomical structure location, extract features, detect lesions, and assign a diagnosis with an appropriate management plan to generate a report accessible to the referring physicians. An ophthalmologist then reviews the images and the diagnostic report, and provides validation before the

422

Automating the Diagnosis, Stratification, and Management of DR

Fig. 14.1. Design for an HIPAA-complaint ocular telehealth network.

confirmed report is returned to the end user. An ophthalmologist can also override the computer-generated auto-diagnosis by manually retrieving the patient information and assigning an alternative diagnosis through the web interface to generate a confirmed report and management plan.

To provide secure data communications between the clinics and the diagnostic server over the Internet, and to meet HIPAA compliance requirements, all data transmission between the client and the server is performed using a cryptographic protocol. To transfer image files to the server and communicate the QA result (pass or fail) to the camera, a secure file transfer protocol (SFTP) is used. To access the report generated on the server, the referring physician can either login on a secure Web site using HTTPS (HTTP over a secure socket layer), or download an encrypted PDF report sent as an e-mail attachment.

To ensure the security of patient-sensitive information, data encryption is performed in the reviewing and confirmation process. When the review is complete, the ophthalmologist digitally 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

423

Thomas P. Karnowski et al.

teleophthalmology clinic. PDF encryption with an issued certificate guarantees data integrity and security, while providing password-protected access of the report to physicians as an e-mail attachment.

To fulfill the requirement of robust data transmission in the conditions of unstable network connectivity, the client program on the camera closely monitors the network status and reacts to the connectivity instability in a real-time manner. In the multi-thread client-monitoring program, one of the threads is devoted to scanning the network status constantly and reporting the network status to the user every second. Once connectivity issues occur, the client program reacts promptly. If the clinic is not transmitting any data when network connection to the server is lost, the client program first issues alert information to the user, then attempts to reestablish network connections for self-recovery. If self-recovery cannot be achieved within a preset time limit and the network problem persists, the client program prompts the user to fix the problem. If there is data transmission when network problems occur, the client program retransmits the exported images after the network status becomes normal. To guarantee that there is no loss of data in the client-server communication, a persistent acknowledgment is required for committed storage in the bidirectional data transmission.

Reliability and data integrity are critical in designing the ocular telehealth system. Reliability requires the completeness, timeliness, and accuracy of the data; integrity refers to the validity and consistency of the data. Reliability and data integrity can be compromised in a number of ways, for example, human errors when data is entered, errors that occur during data transmission, viruses, hardware malfunctions, and natural disasters. To minimize these threats, the following methods are incorporated in the system design: regular backup to independent physical media in the university server farm, control of data access via security mechanisms, design of user interfaces that prevent the input of invalid data, and bookkeeping of the critical status of every procedure or GUI-involved data manipulation to provide an audit trail of data flow in the system.

14.1.3. Workflow

The workflow of the ocular telehealth network is illustrated in Fig. 14.2. The modules in the detailed workflow diagram can be categorized into three

424

Automating the Diagnosis, Stratification, and Management of DR

Fig. 14.2. Workflow schematic design for an HIPAA-complaint ocular telehealth network.

groups: graphical user interface (GUI) modules (shown in yellow blocks in the diagram), data storage (shown in green), and procedural modules (shown in pink). The GUI modules include three parts: the application GUI for the remote clinics, the web GUI for the authorized physicians, and the service GUI for monitoring and bookkeeping. A detailed discussion of the GUI design will be provided in the next section. Data storage can be either in file format inside directories or in a database format. The procedural modules include quality check and thresholding, a diagnostic engine running to perform disease stratification for incoming data and to build a CBIR library based on archived data, report generation, and bookkeeping functions such as log and process records.

Three mechanisms for data communications are involved in the network flow. The communication between the remote clinics and the server is through VPN or SFTP connections; the generated report is communicated back to the referring physicians in an encrypted e-mail, and the communications between the data storage modules and the procedural modules are completed through internal UHTSC network.

425