Hair loss most commonly affects the scalp, being one of the most felt and troubling issues. The market for hair restoration interventions is projected to surpass USD 2,112.7 million by 2024. A variety of hair loss treatments are coming forward to address these issues. Researchers have attempted to automate procedures such as hairline determination and smart harvesting of hair follicles. Robotic systems not only transfer the grafts in the same way humans do but also create the recipient sites for transplantation. The robotic system is revolutionizing hair transplants by being able to pick up, cut, and plant hair follicles in the scalp. Robotic procedures bring technology to surgery, using a robotic arm.
The system learns and, in the process, is able to render the robotized systems more nimble, making judgments and predictions that are difficult for a robot to achieve at high speed. Many commercially available systems from different surgical subfields work based on autonomous technology that includes techniques with artificial intelligence. However, robotic hair transplantation, being a marriage of technology and an inherently human touch, explores the historical context, newer state-of-the-art technology, ethical considerations, and all other aspects, offering a challenging perspective on the blending of technology in the domain of care, even though it is focused on personal care. We illuminate the history, technological improvements, difficulties, precision, sufficiency, and limitations of hair transplantation through robotic systems. Even if robotic hair transplantation is a human-augmented technology, the first president of the International Society of Hair Restoration Surgery thinks that the hair restoration surgery is one of the most profound aesthetic surgical procedures a blind man can perform.
History of Hair Transplantation
Hair transplantation has a long and fascinating history, developing from early attempts like scraping the scalp and applying seed grafts to follicular unit excision, transfer, and robotic hair restoration. Hair restoration surgery is attributed to a Japanese dermatologist who used small punch grafts to repair scars in the scalp and eyebrows and to restore missing hair. His pioneering work was considered the first hair transplant. In 1952, a dermatologist trained with an interest in hair performed the first successful scalp hair transplant on a burn patient. He removed a section of scalp from the occiput and transplanted hair to the burn scar, showing that the transplanted donor hair had a property that caused it to be immune to the effects of dihydrotestosterone. Early hair restoration techniques included treating hair as a random, disorganized mix of seed grafts and punch grafts. Over time, hair transplants began to look more natural as techniques used smaller grafts, where they refined and standardized the process of extracting grafts that preserved the follicular unit and its associated glands to care for patients. Within the early 2000s, follicular unit transplantation, loop magnification, and improved instrumentation set the standard for hair transplants and led to successful hair growth. The transition from traditional hair transplantation to robotic hair restoration technologies telegraphed a sea of change that has since spread rapidly and efficiently, placing new hair on the heads of millions of people seeking modest seed density. Attitudes towards hair restoration have also evolved. Up until recently, people suffering from hair loss were stigmatized. However, hair transplantation surgery has been bookmarked and is now commonly mentioned by celebrities.
Robotic Technologies in Hair Transplantation
Robotic technologies have been making their way into the hair restoration field with the intention of improving treatment outcomes. There are two main robotic systems specifically aiming at non-invasive techniques for serial extraction or follicular unit extraction and sequential or traditional implantation processes. The finest art of this combination needs detailed anatomy besides artistic skills. Automated extraction systems are used for physiological units, which are located in a specific region called the donor area. These automated systems are vacuum-based and designed for follicular unit extraction and are equipped with a flat punch assembly at the distal end. Based on the methods and purpose, they are categorized into narrower “multi-tool” and wider “single-wider” punch systems. These are time-saving extraction systems resulting in the highest possible graft quantity with the poorest transaction.
On the other hand, automated implantation systems are designed for accurate and rapid implantation of follicular units into the recipient area of the scalp. They have gained popularity in improving the outcomes from surgery with reduced surgery time. They implant from 10 to 12 follicular units per second, but are available with varying capacities. Implantation is performed by using an implanter tip, and the force needed to be very light when implanting, possibly between 0.75 and 1.0 Newton. These tips are available in different sizes according to the graft size to be implanted. Robotic hair restoration is a sophisticated and highly intuitive procedure; as such, the use of robotics signifies the advent of a new era in the field. It is no longer confined to a traditional practice, but it has evolved with advanced techniques, increasing better survival graft yields and minimal post-surgical complications besides achieving surgeons’ artistic dreams. Improvement of robotics focuses on dynamics as well as smart therapy decisions.
Automated Extraction Systems
Automated extraction systems are the basis of robotic hair transplantation. These systems are structured on the revenue-share model, which allows clinics to recoup the cost of the automated extraction system and related consumables. Thus, in addition to applying minimally invasive processes, robotic hair transplantation lowers the barriers to entry for new clinics, ultimately increasing the capital flows required to take the process to the next level. Currently, robotic hair transplantation is based primarily on the FUE method, which can be performed manually or, more recently, automated. These systems utilize cameras to perceive the orientation of the hair and the elasticity of the skin. They also interface with drills to extract the follicles.
Several providers have developed systems and protocols for automated and/or robotic FUE. The technology has the potential to speed up the process and minimize damage to the hair when compared to manual FUE and conventional FUE. For instance, as with manual FUE techniques, less damage may occur when the hair is extracted compared to linear strip FUT. Given the precise nature of the process and the relative precision of automated tools, automated procedures are seemingly well-suited to the gradual process of robotic FUE. Other automated systems use vacuum technology to stabilize the scalp and provide two-dimensional control for the system. These systems tend to replicate the human-operated motorized FUE. In several clinical studies, the experience of patients following manual and motorized FUE system procedures has been reported. In these studies, patients reported both of these procedures to be relatively painless and preferred the motorized system experience. Although vacuum may provide better stability than manual control, both methods are not ideal for controlling the angle of incision in the scalp, making it difficult to target those follicles in the subdermal skin layer. Automated systems minimize such errors. Since one of the goals of robotic-assisted harvesting is to produce a high harvesting rate, targeting the follicles in the top part is paramount, as these are primarily the only viable follicles. Overall, if primarily viable follicles are taken, the eventual transplant outcome will be superior as well. Tumor removal systems use a vacuum to provide several types of intrinsic and electrosurgery. These systems have a few limitations, such as the two-dimensional control and the need to sweep the device in coordination with incisions, yet they provide an integrated systematology to the entire process of hair extraction. Technological limitations of these systems include variations in wound geometry and the use of high-intensity electro energy or the use of high-blunt disk cutters. A few mechanized systems with vacuum can provide a low-level high-quality replication of intra-operative freehand FUE. All technologies use haptic feedback error correction mechanisms that feel the proximity of hard barriers on the inside and outside of the skin. These mechanical systems also show promise in their pre-computer vision follicle harvesting, providing an improved intra-operative alternative to clinicians’ manual technologies. In trials, we display our success with a relatively high approximated rate per session for viable follicle extraction with clinical tools of this kind.
Automated Implantation Systems
With advances in image recognition, automated planners for maximum coverage of the recipient area can be designed. Sometimes, so-called nested or ciliary placement should be carried out, where prime direction hair shafts are planted at perpendicular or slightly acute angles to the skin surface and curved hair follicular units at peripheral sites. This consideration is mainly facilitated since the extraction technology does not always cater to the distribution of follicular angles changing from the front to the back. The process is laborious, tiring the team; it is advised to lay the patient back on a 30-degree adjustable footrest so that the distance between the operator and the patient’s crown does not make manual implantation difficult. This removes the need for constant refocusing during the procedure as the focus slides away from the operator.
Considering that U-Punch will extract any long length, heavily anagenated, young age-donor hair follicular units that are destined to grow like natural hair, it makes no difference to an entrepreneur if the extracted hair shafts can find aesthetic utilization in eyebrows, scalp, and beard; or as an autologous component in knee repair surgery; in the treatment of vitiligo; or as a component of a hybrid Achilles tendon—a “bio-Achilles” implant. Designing and improving the automating technology required close collaboration with experienced operators. Automated implantation systems accurately place the extracted grafts while complementing the precision and consistency acquired during extraction into the recipient area. The recipient area and the extracted follicle placement are illustrated on a touchscreen computer system. There are many robotic and a number of non-robotic automated implantation systems being developed internationally. The computerized imaging software system used to aid hair implantation accepts the operator’s visual field and the desired scar line location, evolving from early software that assisted in visualizing recipient density in body hair transplants. Automatic implantation of a large number of follicular units with multiple and single grafts in androgenic alopecia. This system auto-calculates where each extracted unit was placed and where it is to be inserted, planned in comparison with the current state of the art at the time of writing. One handpiece and two removable inserters (one being replaced). This system eliminated sagittal implantation and provided close spacing with parallel multi-unit platinum density without en masse pivoting of the hair root into the skin. However, there were inconsistent exit angles, variable exit puncture diameters, and it was not possible to make an exit hole before placement, just the recipient hole. All would damage the delicate implantation needle embedded hair root. A nimble-fingered technician is desirable, but it is not intrinsically difficult, using magnification.
Artificial Intelligence in Hair Transplantation
Investigation of the used AI techniques began in 2019. Results showed that AI targeted decision level, operation level, and operation stage in 21.4%, 64.3%, and 14.3%, respectively. It facilitated better decision-making, higher surgical precision, and more personalized outcomes. Tools have also been introduced for matching the expectations of patients with their biological needs. These matched patients achieved a higher transected hair for accumulated million punch action density and a greater percentage of grafts containing 3–4 hairs. One study even used machine learning to investigate if any variables can predict the success rate of a follicular unit extraction robot in the donor harvesting procedure.
However, the current literature has not explored all therapeutic and research applications of AI. Robotic and AI-enabled hair transplantation procedures suffer under the same limitations previously described. However, it is fair to suggest that the possible injectable and selective punch discrimination capacities of the AI tools could help to decrease some of these limitations. The quantity of hand-held punch transplantation procedures would stall, and future research in the subject would benefit if AI could be programmed to identify the optimal punch depth and angle for each specific hair, which is a newly identified field. The main limitations of using the AI tools are that appropriate read-only access to patients’ data should be considered to avoid breaches of patients’ privacy and device malfunction, misuse, or information manipulation, even though the corresponding protocols exist. Regulatory and ethical hurdles should be taken into consideration, such as possible misuse of data. The possibility of patients obtaining AI-enhanced devices and conducting self-transplant procedures might also be considered.
Machine Learning Algorithms
Machine learning uses algorithms that can analyze new data to identify patterns and develop predictive models. Researchers in the medical field have gained ground over the last decade regarding the applicability of machine learning. In fields such as hair transplantation, the potential for machine learning to yield useful, data-driven insights to improve outcomes has gained attention. Applying data from large numbers of hair transplantation patients, machine learning algorithms are designed to find patterns within the data that are associated with better or worse surgical outcomes. Once it is trained using historical data, machine learning can be employed to analyze patterns in new patients. It has been effectively used in different medical fields, including organ transplant. Machine learning offers potential insights to better understand which patients may respond to surgery, drug therapy, and even assist patients in finding the ‘right’ type of surgery or drug that is tailored to their disease and genetic makeup. Having access to potentially millions of data patterns, newly developed algorithms are likely to be better than conventional forms of data analysis. The data used to train the learning algorithms to recognize these patterns often goes back decades and is called retrospective data. Once trained, the artificial intelligence knows how to quickly and accurately analyze new patients. The AI models built by machine learning can analyze new patients within seconds using between 5,000 and 10,000 individual variables; this is certainly beyond the capabilities of human doctors. However, the data used by AI have large variations, noise, and sparseness. Also, there might be confounding factors, and some of these factors might degrade the accuracy of modeling. In addition, AI models will be out of date and need to be retrained if they are not trained on the current data. Machine learning outcomes will be different, depending on the training and test data.
Computer Vision Technologies
Computer vision-based image analysis technologies have brought hair transplantation to a new edge. Especially, the capabilities of computer vision in detecting hair follicles and analyzing the angles of hair follicles would increase the precision of extraction and implantation. Furthermore, it would greatly help surgeons make decisions during hair transplantation. For example, computer vision technologies may provide angiogenic assessment in real time by perfusing images. Many other imaging techniques that can obtain real-time 3-D depth information might map the recipient area and visualize it better for a more realistic hairline design. Once the hairline has been designed by the surgeon, the visualization tool will overlay the design to see if it is satisfactory from a patient/doctor viewpoint. Concomitantly, based on how many grafts are available for surgical transplantation that meet the demand of the receiver site and which grafts are unsuitable from a distributional perspective, this will also determine the exact graft or hair-to-skin perforating angle for a more even implantation orientation for a better aesthetic look. Preliminary study results have confirmed this concept.
However, there are also challenges for computer vision technology to penetrate widely across diverse skin types, colors, and conditions. Nevertheless, successful implementation of computer vision in the clinic has demonstrated the potential of these tools. Moreover, when combined with AI constructs such as intelligent workflow through various database integration, accurate predictions would be possible. Thus, computer vision technology can improve precision and safety during the surgery, decreasing the time frame as well. It is not only limited to skin but also to soft-tissue interfaces used for implant design and placement in plastic and cosmetic surgery. In short, hair transplantation is mainly predicated on three steps: namely, follicular unit harvesting, graft dissection, and implantation. Established hair transplantation systems rely heavily on the skills and expertise of a surgeon. These manual procedures could be replaced by robotics-assisted FUE.
Benefits and Challenges of Robotic Hair Transplantation
Robotic hair transplantation has its benefits, but it also comes with a unique set of challenges.
Benefits
Hair transplantation robots have been designed to offer several benefits over manual FUE surgery. First, they claim to offer improved precision when compared with other non-robotic devices. In theory, improved precision would be expected to result in less damage to follicular units, faster wound healing for the patient, reduced recovery times, and better overall outcomes. Additionally, the automation of both graft extraction and implantation is expected to reduce the physical and mental strains of hair transplantation surgery. A system designed to automatically extract grafts will lessen the surgeon’s physical effort during surgery, who otherwise needs to maintain good posture and transmission of energy to the hand-held device. Moreover, the repeatability of a robotic system makes the procedure less dependent upon the human operator, potentially broadening the number of surgeons who are able to safely deliver the procedure.
Challenges
While robotic systems offer some clear benefits, they also present a number of challenges. Robotics research and development often incur high financial costs, and the cost of robotic FUE transplant procedures can be a barrier to entry for surgeons desiring to adopt the technology. Similarly, the equipment and upkeep costs of the technology may only be feasible within large centers. Finally, surgical robots require rigorous training programs for the physicians who will operate them. While surgeons already need specialized training in hair restoration, the addition of robotics training may present a need for novel centers of education, including both universities and private companies. Moreover, testing and approval for both the robot and the supervision of developing surgeons through supervised surgery and mentorship programs are not standardized. Additionally, unique ethical considerations arise with robotic systems. These may include discussions of patient autonomy, informed consent, full disclosure of available options to the consumer, the evolving role of the technological reliance of medicine on a rapidly changing market, and the regulatory environment of second and third wave products. It is essential that these issues be fully discussed and addressed in order to ensure maximal utilization of robotic systems.
The Future of Robotic Hair Transplantation
Today, the fields of hair science and hair restoration continue to grow in transformative ways, raising new possibilities. The future promises further advancement in the integration of artificial intelligence, surgical robotics, and a data-rich practice model. One can envisage improved, highly efficient surgical robots working alongside humans to deliver hair restoration procedures seamlessly. New versions of these robots will feature continuous learning algorithms that make them smarter year after year. Moreover, we are expected to see increasing advancements in hair science and hair restoration towards actual hair multiplication becoming a reality, which could change the paradigm of hair transplantation. In the United States and around the world, regulatory authorities will no doubt want to have their say concerning what is accomplished in this exciting space.
In conclusion, robotic hair transplantation is a process that can truly benefit from the talent of software engineers and healthcare providers working together. We have set a high standard for ourselves and we will continue to invest in robotics and software. We welcome any engineering talent interested in developing technologies for the good of patients to explore enhancing the process with us.
All over the world, hair science and hair restoration have been advancing at a fast pace. The idea of a future with megasessions, where the head can be covered with the highest density possible, raised a new question from our tech team: At this density, how long will a Norwood 7 patient operation take with robots? The calculated time was 200–300 hours. The possibility of designing and launching in the market a dedicated programmable printer may accelerate such a strategy on a quite large scale. Devices may be programmed for very specific needs, based on information that is already available, but even with mechanisms or devices that can provide information that were absolutely not available until today. In any case, considerable investment in advanced R&D will undoubtedly be undertaken to create a marketable product and a new way of managing hair transplantation.
We aim to further develop in the direction of artificial intelligence, surgery automation, and better symbiosis between the machinery and the human touch. The concept of a surgeon/hair technician looking into a microscope to create miniaturized grafts that could be implanted in a new generation of hair surgery robots dedicated to megasessions, analyzing with the high-resolution microscope the individual pictures of follicles and hairs so that the robot can harvest the macrografts with their connective tissue (ensuring good hydration and no desiccation at the root), create the individual graft micro-units, and determine the eventual eschar fraction. Then, thanks to the hair shafts’ orientation and reflection on mostly white dermis and on dry edge irritated tissues of our patients, let the surgeon verify grafts’ survival, angle, angulation, direction, and placement (usually in the area of maximum landing density) before the eschar has formed (usually at the end of the first day), but after drying of the skin, reflection can bring extremely good accuracy to the new robotic task of the future. All of this seems quite far off, but venturing into this imagination excels the entire innovation and research department to move forward in the direction of science and progress. It sparks our desire to believe that robotic hair transplantation is already an extraordinary scientific approach that strives to achieve technological leaps in satisfying and gratifying the need for hair restoration in patients.