IEEE PULSE presents

An Eye Toward Improving

Feature July/August 2015

In ophthalmic anesthesia administration prior to surgeries for problems such as cataracts or glaucoma, the training of new doctors, as in other surgical specialties, is based on a model in which a student trains on a patient under the expert supervision of an experienced doctor. However, the traditional apprenticeship approach is problematic in eye surgery because ocular structures could potentially be damaged by those administering ophthalmic anesthesia for the first time. Apart from the obvious risk to patients, there is also the inability to quantify the performance of a trainee. One solution to these problems might be found in the development of a manikin-based ophthalmic anesthesia training system that can emulate the human ocular anatomy while providing real-time quantitative and qualitative feedback to the trainee during training.
The Measurements and Instrumentation group at the Indian Institute of Technology Madras (IIT Madras) is proposing such a model developed over the course of the last three and a half years. The model has already been tested at the Sankara Nethralaya Eye Hospital in Chennai, India, with promising results.

FIGURE 1 - An overview of the process of regional anesthesia delivery to the eye. The needle is penetrated through the skin at the inferotemporal quadrant of the eye. It is maneuvered to avoid needle injury to the globe or extraocular muscles. The anesthesia is then delivered at a safe rate, and the needle is retracted back along the same line. These techniques require several hours of practice to master, and presently, training is carried out on patients. (Figure used courtesy of [1].)
FIGURE 1 – An overview of the process of regional anesthesia delivery to the eye. The needle is penetrated through the skin at the inferotemporal quadrant of the eye. It is maneuvered to avoid needle injury to the globe or extraocular muscles. The anesthesia is then delivered at a safe rate, and the needle is retracted back along the same line. These techniques require several hours of practice to master, and presently, training is carried out on patients. (Figure used courtesy of [1].)

First, to understand the problem, it is important to know that most ophthalmic surgeries are performed under regional anesthesia. The anesthesia is typically delivered to the eye with a 23-gauge 1- or 1.5-in needle as shown in Figure 1. The needle enters the orbit at the inferotemporal quadrant of the orbit and is maneuvered to the desired location and depth. The anesthetic agent is then injected at a safe rate into the orbital space. A supplementary block is also administered to block the muscles of the medial and superomedial quadrant of the globe. The aim of ophthalmic regional anesthesia is to obtain akinesia and analgesia without injuring any ocular structure in the process. The orbit is a tightly confined space with the six extraocular muscles, the globe, the optic nerve, and other vasculature. It requires substantial practice to master maneuvering the needle in this space without injuring the ocular structures.
In an effort to develop a new training method that would allow students to practice the various regional ophthalmic anesthesia techniques, we started with the design of an anatomically accurate model of the human eye, extraocular muscles, and orbit. Computer-based three-dimensional (3-D) models of these structures were generated from computed tomography scans of human subjects and then modified to provide provisions for mechanical fixtures for ease of assembly (see Figure 2). The 3-D models were then 3-D printed to produce physical models of the relevant ocular anatomy. A matching facial structure was made of high-hardness silicone to reduce the cost and to provide an underlying structure of the human face. A silicone skin was laid over the anatomically accurate orbital structure and the matching structure.
FIGURE 2 - The capacitive sensing scheme developed to detect needle proximity to and touching of ocular structures. The 3-D models of the eye, muscle, and orbital structures with provisions for mechanical and electrical connections are also shown. The computer models were 3-D printed to produce physical structures for the manikin. A silicone facial structure and skin overlay were added to give the manikin a realistic look. (Figure used courtesy of [1].)
FIGURE 2 – The capacitive sensing scheme developed to detect needle proximity to and touching of ocular structures. The 3-D models of the eye, muscle, and orbital structures with provisions for mechanical and electrical connections are also shown. The computer models were 3-D printed to produce physical structures for the manikin. A silicone facial structure and skin overlay were added to give the manikin a realistic look. (Figure used courtesy of [1].)

After we had faithfully reproduced the human anatomy, the next challenge was to integrate a sensing system capable of providing real-time information on the proximity and touch of the needle to these anatomical structures in the manikin. Optical and magnetic sensing systems were not feasible due to the lack of a direct line of sight and the extremely confined space inside the manikin. A capacitive sensing scheme, as shown in Figure 2, was developed where the needle of the syringe was connected to a sinusoidal voltage source, thus acting as a capacitive transmitter electrode. On the other hand, in the physical model of the eye and muscle structures of the eye, the extraocular muscles were coated with a silver conductive ink, thus acting as capacitive receiver electrodes. Electrical connections were drawn from it, and the displacement current from each electrode was passed through the appropriate signal conditioning circuits and sampled using a data acquisition system (DAS). Finally, we processed the sampled information using a LabVIEW-based virtual instrument.
To understand the working of the capacitive sensing scheme, one must assume that the needle has entered the orbital space, as shown in Figure 2. Several capacitances are formed between the needle and the muscle structures. These capacitances vary according to the relative position of the needle to the respective muscle structure, and the displacement current received by the particular muscle electrode also varies accordingly. Hence, if the needle approaches a muscle, the displacement current corresponding to that particular muscle electrode will rise due to an increase in the associated needle to muscle capacitance, and this information can be used to detect the proximity of the needle to the muscle structures. The virtual instrument determines the needle proximity and touch information and displays it to the trainee in real time through an intuitive graphical user interface (GUI).
However, the human ocular anatomy varies from person to person, particularly due to myopia and hyperopia, which tend to cause large variations in the axial length of the eye. Myopic eyes tend to have a larger axial length, putting them at a significantly higher risk of needle injury to the posterior of the globe. Hence, it is useful to make the eye structure of the manikin modular so that it can be exchanged for eye structures with abnormalities/pathologies as may be required for training. The previous sensing scheme requires electrical connections to be drawn from the eye structure, hence making it difficult to replace. Also, the depth of penetration of the needle in the orbit is an important measure that determines the spread of the anesthesia and its effectiveness in blocking all the muscles of the eye. Therefore, it is important to provide the trainee with an indication of the depth of penetration of the needle during training.
To further enhance the training system, we developed a new capacitive sensing scheme to incorporate the features mentioned previously. In the new scheme, the muscles in the eye structure of the manikin are coated with a conductive ink, but electrical connections are not drawn from it, thus rendering it replaceable. On the other hand, we modified the orbital structure to have a wall-like structure to enable the placement of electrodes on its outer surface parallel to each of the muscles. Each muscle has a set of four electrodes—three corresponding to the three depths of penetration to be detected (top, mid, and deep) and a fixed electrode as shown in Figure 3. The top, mid, and deep electrodes are sequentially excited by a sinusoidal voltage source, while the fixed electrode is always excited by a phase-shifted version of the sinusoidal voltage. The needle of the syringe acts as a capacitive displacement current-receiving probe and is connected with a shielded cable to a robust signal conditioning and DAS. The amplitude and phase of the received current are then measured to determine the depth of penetration, proximity, and touch information in real time and displayed to the trainee.
FIGURE 3 - The capacitive sensing scheme was enhanced to detect the depth of penetration of the needle in the orbit. The scheme does not require any electrical connections from the globe or muscle structures, rendering it replaceable to emulate various pathologies. The figure shows the modified orbital structure 3-D models developed for the capacitive scheme. Electrodes are placed on the outer wall of the orbital structure in specialized holders as shown. The 3-D-printed physical structures with the connections from the copper electrodes are also shown. (Figure used courtesy of [2].)
FIGURE 3 – The capacitive sensing scheme was enhanced to detect the depth of penetration of the needle in the orbit. The scheme does not require any electrical connections from the globe or muscle structures, rendering it replaceable to emulate various pathologies. The figure shows the modified orbital structure 3-D models developed for the capacitive scheme. Electrodes are placed on the outer wall of the orbital structure in specialized holders as shown. The 3-D-printed physical structures with the connections from the copper electrodes are also shown. (Figure used courtesy of [2].)

By training on the developed system, the trainee will be able to enter the orbital space without injuring the ocular structures. However, it is also vitally important that the rate of injection of anesthesia be within a safe limit to avoid complications and reduce the pain perceived by the patient.
To address this, we developed a magnetic sensing scheme to detect the rate of injection of anesthesia during training. As part of this scheme, we designed a special piston to house a Hall-effect sensor on one end of the piston as shown in Figure 4. Ring magnets were attached to the fixed syringe body such that the central axis of the magnets and the detection axis of the sensor were parallel to each other. As a result, when the piston is moved, the magnetic field sensed by the sensor varies according to the distance between the piston and the syringe body. This distance is directly proportional to the volume of liquid present inside the syringe. The sensor output voltage was calibrated to map the volume of liquid in the syringe. Furthermore, the rate of injection was calculated by taking successive samples of the volume information and dividing it by the time period between the samples. We integrated the instrumented syringe to the manikin-based training system and displayed the rate of injection to the trainee on the GUI, and the trainee was informed whether it is in the safe range or not. The needle of the syringe was blocked to prevent the passage of liquid from the syringe into the manikin as this could cause damage to the manikin or its associated electronics. However, to provide the illusion of liquid flow from the syringe, the special piston was designed to be opaque and a passage hole provided in its rubber seal. This allows for the free flow of liquid into the opaque piston body when the piston is pushed, thus hiding it from the view of the trainee.
FIGURE 4 - The Hall-effect-sensor-based rate of injection detection module. The specially developed piston houses a Hall-effect sensor while ring magnets are attached to the fixed body of the syringe. As the piston moves, the magnetic field sensed by the sensor changes as a function of the distance between the sensor and the magnet. This distance is directly calibrated in terms of the volume of liquid in the syringe. The rate of injection is calculated by differentiating the volume of liquid with respect to time. (Figure used courtesy of [1].)
FIGURE 4 – The Hall-effect-sensor-based rate of injection detection module. The specially developed piston houses a Hall-effect sensor while ring magnets are attached to the fixed body of the syringe. As the piston moves, the magnetic field sensed by the sensor changes as a function of the distance between the sensor and the magnet. This distance is directly calibrated in terms of the volume of liquid in the syringe. The rate of injection is calculated by differentiating the volume of liquid with respect to time. (Figure used courtesy of [1].)

The integrated manikin shown in Figure 5 was validated at the Sankara Nethralaya Eye Hospital, a large tertiary ophthalmic hospital. Thirty-one postgraduate students of ophthalmology with little or no prior experience in delivering anesthesia were asked to perform blocks on the manikin first without and then with the intelligent real-time feedback while they were automatically graded by the system. A large change in the training scores was observed only when the real-time feedback was provided during training. This indicates that the feedback provided by the system is effective in improving training outcomes in ophthalmology trainees and can potentially have a huge positive impact in lowering the complication rates in ophthalmic anesthesia.
FIGURE 5 - The fully assembled ophthalmic anesthesia training system showing the manikin integrated to the rate of injection detection module. The trainee can be seen administering a block on the manikin while the GUI provides real-time information of the needle’s proximity to and touching of ocular structures. The system was tested at a large tertiary ophthalmic hospital where it was demonstrated to be highly effective in improving training outcomes of ophthalmology trainees. (Figure used courtesy of [1].)
FIGURE 5 – The fully assembled ophthalmic anesthesia training system showing the manikin integrated to the rate of injection detection module. The trainee can be seen administering a block on the manikin while the GUI provides real-time information of the needle’s proximity to and touching of ocular structures. The system was tested at a large tertiary ophthalmic hospital where it was demonstrated to be highly effective in improving training outcomes of ophthalmology trainees. (Figure used courtesy of [1].)

References

  1. B. Mukherjee, B. George, and M. Sivaprakasam, “An ophthalmic anesthesia training system using integrated capacitive and Hall-effect sensors,” IEEE Trans. Instrum. Meas., vol. 63, no. 5, pp. 1153–1106, May 2014.
  2. B. Mukherjee, B. George, and M. Sivaprakasam. A multi-electrode electric field based sensing system for ophthalmic anesthesia training. IEEE Trans. Biomed. Circuits Syst. [Online].
  3. B. Mukherjee, B. George, and M. Sivaprakasam, “A syringe injection rate detector employing a dual Hall-effect sensor configuration,” in Proc. 2013 35th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), 3–7 July 2013, pp. 4734–4737.

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