“The doctor of the future will give no medicine, but will instruct his patients in care of the human frame, in diet, and in the cause and prevention of disease.”
– Thomas Edison
With today’s busy yet flexible lifestyle and greater awareness, wellness has increasingly gained priority in people’s lives. People are more willing to spend time on different aspects of their overall health. Availability of information virtually spreads cognizance amongst customers, helps them in better decision making as well as delivery of chosen products at their doorstep.
For prevention is better than cure, multiple clinical trials are conducted for developing products like cosmetics and drugs. This data is collected over a specific period of time with minor changes in developing conditions, ingredients or so. Thereafter, such data analysis in clinical research proves to be the basic stepping stone towards gaining approval from FDA, CDER or any other established authority.
Moreover, the clinical data analysis market had a reported market size of USD 14.7 billion in 2022 and is expected to reach a market value of about USD 93.8 billion by 2028, surging at a CAGR of approximately 35.8% during the period 2023-2028 [IMARC Group]. Therefore, clinical data analytics are likely to gain momentum in the future, and would prove to be a worthy investment.
Adding a Tinge of Technology
With the prevalence of deep learning and generative AI, accuracy of data collection and analysis applications has increased manifolds, as has the availability of open source datasets. With the correct harnessing of data, there are endless applications which can be targeted in different healthcare and wellness domains. This includes skin care, hair care, fertility treatments, initial medical diagnosis including those of X rays. Such domain based statistical analysis in clinical trials paves a pathway towards its success. Getting subjects to clinical trials is a costly affair. Clinical data collection companies have started mobile applications for subjects to collect pictures, be it facial or hair analysis pictures in their homes on a daily basis and those images are sent to clinical analysis centers over the internet for further analysis.
Types of Data collection and Analysis
There can be a number of ways of clinical data analysis depending upon various industries. Given below are a few domain specific applications of clinical trial data analysis and methodologies for the same.
Clinical Skin Analysis
As mentioned earlier, most of the current clinical data collection is happening remotely, where the subject captures his/her facial skin images, which are then sent to the clinical data collection centers for further analysis. As the images are captured by the subjects, who are not well trained in data capture process, issues like improper pose, improper lighting, occlusions creep into the images captured by the subjects. The AI based applications can be used in this context to improve the consistency in the data capture. A guided selfie helps the subjects to capture the images at high resolution so that it is not degraded by external factors such light, obstructions or humanistic variations such as pose or expression.
AI models and algorithms are then used to do an in-depth skin analysis on the images captured. The analysis can give information about different skin features such as wrinkles, spots, pores, elasticity, shininess and yellowness. Using these features, different skincare products can be suggested to users. Also, the effectiveness of the recommended skincare products can be verified by taking selfies in a defined manner across a frame of time (maybe day every day or week for a few weeks) to see the difference in demarcated features. These skin products can also be customized for particular skin tones by first carrying out a skin tone analysis.
Clinical Hair Analysis
Haircare can be for styling or to diagnose a medical condition. Similar to skin analysis, effectiveness of haircare products can be checked through hair image capture, data collection and subsequent analysis. Since it is difficult to analyze images captured in varied poses, subjects can be guided through web or mobile based applications powered by AI models for continuity in posture while hair images are clicked at high resolution. External factors obstructing the process such as lighting variations, occlusion, light reflection etc. are minimized to null using such algorithms for good quality analysis.
Depending on the type of application, the capture process would vary. Some applications for hair styling include hair bulk segmentation, hair frizz analysis, hair based alpha matting for hair color transfer. A detailed scalp analysis can lead to detection of hair loss diseases such as alopecia areata and defects such as dandruff and lesions on the scalp. Open source hair datasets can be harnessed for a clinical analysis of hair at a coarser level. For a more in-depth analysis of specific defects, specialized datasets may need to be created. A well-known method of defect related data representation and research is clinical trial data analysis using R and SAS can also aid in some of the data based applications.
Clinical Nail Analysis
It is a common conjecture that many diseases or pathogens present in the body can be detected through change in fingernails’ structure and color, such as Hepatitis B virus, Cyanosis, Leukonychia etc. It is even recognised that early subungual lesions in nails is a known symptom of cancer. Clinical nail analysis can prove to be quite beneficial in detecting early onset of all such cases. High quality images of various nail conditions can be clicked for thorough analysis.
With AI based guidance supported by animations and message cues, subjects are able to capture images of their nails from the convenience of their homes, per say, correct light exposure, bounding region of interest with the help of interactive UI etc., without the need for researchers or doctors to transverse. Post which these images can be processed and high-quality analysis can be received.
It becomes easier for medical professionals, analysts, researchers etc. to observe the subject’s nail surface shape, nail plate, serious health related issues’ symptoms such as Mees’ Lines, Clubbing, Muehrcke’s Lines, Terry’s Nails, Half and Half Nails etc. By training Deep Learning models with fingernail image datasets obtained from subjects, medical diagnostics of chronic diseases are simplified.
Embryonic Image Analysis
With the advent of various infertility drugs, it has become the need of the hour to observe embryonic development in a real time scenario. By developing an interactive interface, an infertile couple can undergo a preliminary online questionnaire round about their health and habits at their homes or at the clinic. On the basis of their choices, they can be recommended by the fertility specialists to undergo certain tests for clinical data collection, followed by sample collection, analysis and in-vitro fertilization process.
Given the high costs associated with Time Lapse embryoscope, a simple and effective method of observing proper growth and selection amongst in-vitro developing embryos is through timed microscopic image based analysis. Alongside verification of such analysis with authorized embryologists, it is hereto, possible to achieve successful IVF procedures across the globe. Meanwhile, it is also easy to pinpoint the causes of failure, cardinal timings and factors for improper growth in rare cases for further studies.
KritiKal Solutions Can Assist You
KritiKal Solutions provides you extensive technical support for conducting certain types of clinical analyses in the Health and Wellness industry. We understand that traversing across the globe for conducting pilot clinic analysis of hair, skin or nail image samples collected from targeted populations can be an expensive and cumbersome process.
In such situations, preliminary image collection can be conducted through web or mobile based image capturing applications, where subjects can themselves click images at their convenient locations and submit them for analysis. For example, in case of skin cream trials, dermatological images of pre and post cream application need to be collected and analyzed on a per day basis.
The image capture process can be hampered due to unsuitable environmental conditions or random facial position. In order to tackle this, image capture guidance tools with animation and vision based cues are utilized for fixing subject’s pose and direction of capture, post which the tool’s capture button gets activated. Moreover, these tools detect obstacles like sunglasses, caps, hair over the forehead (in case of skin analysis), unsuitable lighting settings such as the ones captured while standing near a window and more.
Similarly, in the case of analysis of hair product effectiveness, images captured in different poses can be compared only by hair stylists or analysts. For maintaining continuity in images captured in terms of pose for hair related comparisons, guidance tools can come in quite handy.
We assist in developing such tools for helping subjects capture images of hair, skin or nail precisely and systematically. We have worked with a major multinational corporation in developing applications as well as related hardware for clinical analysis. We can provide an upthrust to your business by increasing your productivity multifold.
Not only we assist you with clinical data analysis, but also assure safeguarding authentic datasets used in such trials against various security vulnerabilities like data leakage etc. Please call us or mail us at firstname.lastname@example.org to avail our services.
From wellness like skincare and haircare as described in this blog, to aiding in initial level medical diagnosis and aiding towards fertility treatments such as IVF, there is very little where deep learning and computer vision can’t help the medical practitioners. This could be at the level of remote monitoring, initial X ray report analysis or even selection of embryos. These are our virtual experts who together with the human experts can take this field forward at an accelerated rate and provide better services to the public. It is rightly said “ What the human eye can see, the computer can detect”.
Dr. Aditi Kapoor holds the position of Technology Head – Computer Vision and Image Processing, North India at KritiKal Solutions. Along with a Phd from IIT-D, she has over 13 years of experience in the field of CV, Machine Learning and Artificial Intelligence. With strong dedication and resolve, she has helped KritiKal in many successful project deliveries for some major companies.