This put up is cowritten with Dr. Mikkel Hansen from Qbtech.
The evaluation and prognosis of consideration deficit hyperactive dysfunction (ADHD) has historically relied on medical observations and behavioral evaluations. Whereas these strategies are beneficial, the method will be complicated and time-intensive. Qbtech, based in 2002 in Stockholm, Sweden, enhances ADHD prognosis by integrating goal measurements with medical experience, serving to clinicians make extra knowledgeable diagnostic selections. With over a million checks accomplished throughout 14 nations, the corporate’s FDA-cleared and CE-marked merchandise—QbTest (clinic-based) and QbCheck (distant)— have established themselves as widely-adopted instruments for goal ADHD testing. Now, Qbtech goals at extending their capabilities with QbMobile, a smartphone-native evaluation that makes use of Amazon Net Providers (AWS) to carry clinical-grade ADHD testing on to sufferers’ units.
On this put up, we discover how Qbtech streamlined their machine studying (ML) workflow utilizing Amazon SageMaker AI, a completely managed service to construct, prepare and deploy ML fashions, and AWS Glue, a serverless service that makes knowledge integration easier, quicker, and less expensive. Qbtech developed and deployed a mannequin that effectively processes knowledge from smartphone cameras, movement sensors, and check outcomes. This new resolution diminished their function engineering time from weeks to hours, whereas sustaining the excessive medical requirements required by healthcare suppliers.
The problem: Democratizing entry to goal ADHD evaluation
ADHD impacts thousands and thousands worldwide, but conventional prognosis typically includes prolonged wait instances and a number of clinic visits. Whereas Qbtech’s current options superior in-clinic and distant webcam-based testing, the corporate recognized a chance to broaden entry by way of smartphone know-how. Qbtech wanted to remodel uncooked digital camera feeds and movement sensor knowledge from various smartphone {hardware} into clinically validated ADHD assessments that would present the identical goal diagnostic worth as their established medical instruments. This required processing complicated multimodal knowledge streams, extracting significant options, and coaching fashions that would preserve accuracy throughout hundreds of gadget variations—all whereas assembly stringent healthcare regulatory necessities.
Constructing the bogus intelligence (AI) mannequin: From uncooked knowledge to medical insights
Qbtech’s method to cell ADHD evaluation makes use of machine studying strategies to course of and analyze a number of knowledge streams concurrently. The group chosen Binary LightGBM as their major algorithm for the ADHD evaluation mannequin.
Determine 1: Finish-to-end knowledge processing and have engineering pipeline for QbMobile ADHD evaluation mannequin
The ultimate mannequin makes use of 24 enter options derived from face monitoring, head motion measurements, error patterns throughout checks, patterns in how customers deal with their telephones, and demography info. This scale was essential to seize the nuanced patterns in consideration, hyperactivity, and impulsivity that characterize ADHD throughout various affected person populations. The group utilized three key frameworks: LightGBM as their major machine studying algorithm, Scikit-learn (sklearn) as their machine studying instrument library for knowledge processing and mannequin growth, and SHAP (SHapley Additive exPlanations) as their methodology to evaluate function significance. These instruments have been chosen for his or her flexibility in dealing with multimodal knowledge and sturdy deployment capabilities. The group used roughly 2,000 samples, with every pattern containing about 50MB of knowledge. Inside this dataset, there was a category imbalance with the minority class representing round 20% of the samples. The info was rigorously break up into prepare and check units utilizing stratification based mostly on each prognosis and demographic options, making certain equal illustration throughout intersectional teams. Extra consideration was given to grouping since some check takers accomplished a number of checks. The group carried out a five-fold cross-validation technique utilizing the identical stratification and group approaches. This complete dataset, derived from Qbtech’s decade-plus medical testing expertise, offered the inspiration for coaching fashions that would generalize throughout completely different demographics and gadget varieties.
Coaching efficiency and analysis
Whereas the precise mannequin coaching requires solely about one minute of computation time, the resource-intensive part was the transformation of uncooked samples into structured options. This preprocessing stage is the place SageMaker AI managed processing jobs offered substantial acceleration, decreasing the processing time for function extraction and enabling environment friendly iteration all through the event lifecycle. To assist guarantee medical validity, Qbtech employed rigorous analysis metrics together with sensitivity (85.7%), specificity (74.9%), and PR-AUC (73.2%). The group carried out nested cross-validation with Optuna for hyperparameter tuning throughout every analysis fold, optimizing for the sum of sensitivity and specificity reasonably than PR-AUC to attain extra balanced errors. These metrics and optimization methods have been rigorously chosen to align with medical diagnostic standards and regulatory necessities for medical units. The group famous that within the medical sector, there isn’t a absolute floor fact in diagnosing ADHD—the gold commonplace is when a number of medical doctors agree on a prognosis. The actual worth of Qbtech’s resolution is offering constant, goal knowledge that brings confidence to clinicians’ diagnostic selections.
Scaling function engineering with Amazon SageMaker AI
A key enchancment in Qbtech’s growth course of got here from implementing parallel processing capabilities on cloud infrastructure. By implementing asynchronous processing that allows every check to run in parallel reasonably than sequentially, the group may carry out downloading, JSON parsing, and have transformation in parallel throughout a number of processes. The function engineering pipeline begins by changing uncooked knowledge into time sequence for every knowledge supply, then producing numerous options from these time sequence. As an example, face place knowledge is processed to compute statistics reminiscent of minimal, most, and imply motion inside 30-second home windows. To attain the discount in processing time from 2 days to half-hour, Qbtech carried out a parallel processing method utilizing Python’s multiprocessing capabilities on Amazon Sagemaker AI:
This operate creates a pool of employees equal to the variety of central processing unit (CPU) cores obtainable on the compute occasion—for instance, on an ml.m5.8xlarge occasion with 32 cores, this implies 32 recordsdata will be processed concurrently. Every employee calls uuid_to_features, which handles retrieving the JSON check file from Amazon S3, parsing the 50MB of accelerometer and face monitoring knowledge, and performing the precise function computation to extract the medical indicators. The outcomes from all employees are then mixed right into a single dataset utilizing pandas’ concat operate.
This parallel processing method enabled a 96% discount in computation time, permitting the group to iterate quickly throughout mannequin growth whereas sustaining the reliability wanted for healthcare purposes. Qbtech reported no {hardware} failures or interruptions throughout their growth course of, permitting them to deal with mannequin enchancment reasonably than infrastructure administration.
Knowledge pipeline: From smartphone to medical choice
The info pipeline begins with uncooked smartphone sensor knowledge in numerous codecs. The uncooked ADHD check knowledge is available in JSON format, containing accelerometer readings, face monitoring knowledge, and checks outcomes. AWS Glue jobs deal with the preliminary extraction and transformation of this heterogeneous knowledge right into a standardized format appropriate for evaluation. These transformations assist preserve knowledge high quality and consistency throughout completely different gadget varieties and working techniques, a essential requirement for preserving evaluation accuracy. Glue jobs rework codecs from uncooked recordsdata into a typical one, changing legacy codecs to new codecs and making the file construction extra pleasant for evaluation (e.g., calculating common values from arrays).
Characteristic extraction and choice
The function engineering course of extracts significant medical indicators from uncooked sensor knowledge. Qbtech extracts roughly 200 options from the uncooked knowledge, with solely 24 making it to the ultimate mannequin. This discount from uncooked options to mannequin inputs was achieved by way of a scientific guide choice course of, the place histograms per label have been analyzed to verify for separation between lessons. The group carried out an iterative method, including essentially the most promising options incrementally whereas monitoring enhancements in cross-validation efficiency. SHAP evaluation was used to confirm that options interacted with the prognosis in clinically significant methods—for instance, confirming that increased values in motion options corresponded to elevated probability of ADHD. The group additionally eradicated options with excessive correlation as one other method to make sure the chosen options have been independently contributing to the prognosis. This methodical function choice course of displays the area data encoded into the mannequin growth. A key problem was decreasing very long time sequence into tabular options whereas nonetheless capturing the important alerts. The group developed strategies to extract clinically related patterns from face monitoring and movement sensor knowledge, specializing in indicators that correlate with ADHD signs.
Finish-to-end latency
For a medical instrument to be sensible, outcomes should be obtainable shortly. Qbtech’s pipeline delivers ends in beneath a minute from knowledge assortment to mannequin inference. This speedy turnaround helps real-time medical decision-making and improves the affected person expertise.
Quantifiable influence: Improvement effectivity positive factors
The first enchancment got here in time-to-result for function engineering, dropping from two days to simply half-hour by way of parallel processing. This 96% discount in wall time enabled the group to finish 20 growth iterations way more effectively, considerably accelerating the mannequin growth cycle.
Scientific influence: Comparative medical efficiency
The medical validation of QbMobile towards Qbtech’s established merchandise reveals promising outcomes. Efficiency metrics point out that the smartphone-based evaluation maintains the excessive medical requirements of Qbtech’s current options. The shift to cell evaluation has modified the care supply mannequin. For suppliers which are solely remote-based, QbMobile permits for a 100% distant diagnostic course of. It permits sufferers who would in any other case not have the ability to take part in an in-clinic evaluation as a result of logistical challenges to obtain correct analysis. This transition reduces boundaries to prognosis and permits extra frequent monitoring of therapy effectiveness.
Deployment and steady enchancment
The manufacturing deployment makes use of AWS companies for reliability and scale. Qbtech packages the skilled mannequin, along with Python code, right into a Docker picture. The Docker picture is then deployed to AWS ECR by way of GitHub releases that set off a GitHub Motion. Lastly, the SageMaker AI endpoint is deployed by Terraform along with the remainder of their backend infrastructure. To take care of constant efficiency throughout units, Qbtech conducts common validation checks throughout growth, analyzing whether or not gadget fashions have an effect on evaluation efficiency in any unintended methods.
Safety and monitoring for healthcare compliance
Qbtech’s deployment on AWS incorporates complete safety and monitoring measures important for healthcare purposes. All knowledge is encrypted at relaxation, and the system maintains affected person privateness by maintaining knowledge nameless —no particular person will be recognized with knowledge saved at Qbtech. The system enforces multi-factor authentication and constantly screens service availability, efficiency metrics, and potential safety threats. All system entry is logged and monitored, with automated flagging of suspicious exercise. This method helps meet healthcare safety necessities whereas sustaining the reliability wanted for medical workflows.
Wanting Ahead: Scaling for international influence
Qbtech’s infrastructure technique anticipates QbMobile’s rising adoption worldwide. The group plans to make use of the elastic scaling capabilities of SageMaker AI to handle any efficiency bottlenecks that emerge with elevated utilization. For mannequin enhancement, Qbtech is implementing annual replace cycles that transcend easy retraining. As their dataset expands, they’ll incorporate new options that seize extra behavioral patterns, constantly bettering diagnostic accuracy and robustness.
Future analysis instructions
Constructing on their present work, Qbtech is exploring extra knowledge streams and sensor inputs to additional improve evaluation accuracy and broaden diagnostic capabilities. They’re additionally in dialogue with regulatory authorities on find out how to implement a steady enchancment plan in mannequin efficiency, which may probably embody utilizing completely different fashions like neural networks. The insights from over 1 million accomplished checks present a singular basis for function calibration and threshold definitions. This data-driven method permits cell assessments to profit from the corporate’s intensive medical expertise.
Wanting past ADHD, the platform reveals promise for broader purposes. Qbtech believes that QbMobile permits researchers to entry knowledge varieties they haven’t had earlier than or had difficulties acquiring. By analysis collaborations, they goal to discover the total potential of QbMobile, Machine Studying, and extra options to influence ADHD and probably different circumstances sooner or later.
Conclusion
Qbtech’s implementation of QbMobile on AWS demonstrates significant progress in the direction of accessible, goal ADHD evaluation. By leveraging the parallel processing capabilities of Amazon SageMaker AI, and the information transformation capabilities of AWS Glue, they’ve diminished function engineering time by 96% whereas constructing a clinically validated AI mannequin that runs on smartphones worldwide.
The influence extends past technical metrics: sufferers can now entry clinical-grade ADHD assessments from their units, decreasing wait instances and bettering entry to care. For healthcare suppliers, the standardized, goal knowledge permits extra assured diagnoses and higher therapy monitoring.
As psychological well being challenges proceed to develop globally, Qbtech’s use of cloud-based AI reveals how trendy infrastructure can broaden entry to specialised healthcare companies. Their method supplies insights for different healthcare organizations trying to make use of AI and cloud computing to enhance affected person outcomes at scale.
To study extra about constructing healthcare AI options on AWS, discover Amazon SageMaker AI and AWS Glue documentation, or contact AWS healthcare specialists to debate your particular use case.
In regards to the authors
Antonio Martellotta is a Senior Options Architect at AWS. He advices Personal Fairness corporations and their portfolio firms on digital worth creation leveraging cloud and AI. His major areas of experience are knowledge technique, knowledge analytics, and Generative AI. He holds a bachelor’s diploma in Biomedical Engineering and a triple grasp diploma in Sensible Techniques Integrations.
Dr. Mikkel Hansen is a Danish-trained medical physician and seasoned healthcare govt. Since October 2020, he has served as Medical Director and CMO at Qbtech, spearheading the combination of goal, data-driven applied sciences—reminiscent of QbTest and QbCheck—into ADHD prognosis and administration. Dr. Hansen is dedicated to bettering diagnostic confidence and effectivity in ADHD care worldwide. Past medical digital well being innovation, Dr. Hansen engages immediately with authorities—together with the U.S. DEA, NICE, FDA, and EMA—serving to to form coverage round secure ADHD prognosis and stimulant use.
