Prof Madhav Rao, Associate Professor & iM.Tech Coordinator, IIIT Bangalore highlights the role of technology in enhancing mental healthcare
Mental healthcare is one of the primary tools required for a large population base in today’s complex world, with its prominence felt especially during the pandemic COVID’19. Mental healthcare not only involves specific aiding tools to lower the mental distress, but also caters towards surgical and assistive drive to lead a better life for an individual with mental health problems. The E-health research centre (EHRC) by IIIT-Bangalore, a centre which delve into all possible health care problems was initiated in 2016. One of the verticals in EHRC focused on developing innovative device solutions catering towards improvement in neurosurgery and providing assistance to enhance the quality of life post-surgery which is often neglected.
Surgical tools to help achieve efficiency in the outcome of the surgery, precision to diagnose, and target removal are highly valuable, however very little emphasis on surgical engineering is seen in our country. Moreover, the robotics and autonomous surgical tools for neurosurgical application is expected to improve the outcome to a large extent considering the operating region is not only extremely less, but also highly sensitive. Dr Vikas Vazhayil, a neurosurgeon and faculty from Neurosurgical department of NIMHANS, who is also an active collaborator for Surgical and Assistive Robotics (SARL) Lab at IIIT-Bangalore, has shared several inputs on completely or partially automating neurosurgical process to yield a successful surgery.
Many procedures followed in neurosurgery resemble engineering methods, and hence, a natural evolution in the field of neurosurgery is adopting robotic engineering principles. Neurosurgery, as compared to other surgeries, requires precision in the order of millimetre range, while operating over a constrained area involving high risk. Robotic surgery has brought about much-needed benefits such as smaller incision, improved precision, and minimum access to sensitive regions in the brain. Hence robotic systems for neurosurgery are expected to reduce human-related errors, improve precision, and perform surgery in highly inaccessible regions. An autonomous neurosurgical drilling tool was designed at prototype level to showcase the MRI/CT image guided targeting system which uses intra-operative imaging to drive the drilling system to a target position in inter-operative patient space. The proposed drilling system is useful for craniotomy applications where the skull thickness is generally uniform near the highest part of the skull bone. Guided robotic drilling is one such alternative towards the contemporary craniotomy method that leads to increased accuracy, thereby minimising permanent secondary damages to the patient under treatment. Image processing technique on the native 3D slices of MRI or CT scan was applied to find the native target location. The autonomous drill positioning system is validated experimentally on a 3D printed human head, and the drill head placement was characterised along with the orientation errors and accuracy in the guided path, showing promising results.
Additionally, a hyperflexible neuro-scope is designed to view the brain regions while operating. The current operating microscope used for neurosurgery is non-flexible, thereby it does not allow to view oblique regions which hampers the neurosurgeon’s perception on the overall layered and depth information to some extent. Hence the designed and developed hyperflexible neuro-scope device is likely to enhance the surgeon’s decision making and ultimately yield a better outcome. The hyperflexible neurosurgical scope in the macro-scale version was validated for viewing brain dissection results as of now. The tilted view allowed capturing of substantial information closer to the selected region of interest, considered as a significant improvement in the neurosurgical operating microscope technology. The neurosurgical microscope successfully demonstrated the angular re-positioning capability of the probe to the saved posture and regained the previous probe view with an acceptable orientation error. The re-positioning capability is considered a vital feature for the neurosurgeon. The flexible microscope in its current design form is best suited for educational training and assessments over dissecting cadaver brains.
My team has also designed a wearable exoskeleton device for upper and lower limb which aids in automating routine physiotherapy exercises. The plan is to enable independent physiotherapy and recover from post stroke upper limb and lower limb deformation. The lower limb robotic device is also applicable for overcoming Deep Venous Thrombosis (DVT) problems for patients with minimal movements or in long duration surgery. Technological innovations have changed the management of disability. Exoskeleton devices have changed the lives of amputees and allow them to lead near-normal lives. However, in the case of neuromuscular disability, progress in orthotic devices has been relatively tardy. This is due to the added complexity of these devices to account for form and physiology of the affected limb. The major challenge in upper limb orthosis is in mimicking and assisting hand function. The hand is one of the most complex organs of the human body. The versatility of hand functions makes it difficult to implement using sets of actuators. Added to it, the technical challenges of having a compact device with a small device profile adds on the complexity of the design. The developed device incorporates all the features which are required for implementing all functions of the human hand. The assembly of actuators and the control systems within the forearm component is an attempt to enable the hand to have a full range of movement.
The design is thus anthropomimetic in that it mimics natural design wherein a substantial number of muscles actuating the hand are sited in the forearm. Each set of pulleys attempt to mimic the tendons which actuate fingers individually. The form factor of each finger is too complex for single sets of actuators to replicate finger function. Hence, multiple strategically placed pulley systems are used for this purpose. Machine learning algorithm was applied to determine the limited muscle movements for which powered actuation is required to complete the action.