More than 7000 rare diseases are currently known to science, affecting approximately 350 million people worldwide. Bingli has developed a new methodology for the development and validation of algorithms for early diagnosis and/or risk population screening. 

» The Bingli AI is trained to recognize the rare disease, either by its presenting symptoms or potential complications that may be induced by the disease. In both cases the rare disease will be shown in the list of suggested differential diagnoses.

» Based on the same dataset, Bingli can also build questionnaires for more targeted screening, for example patients who are visiting a specialist or are already in a specialist’s database. In this case, Bingli will calculate the likelihood that the patient may be suffering from the rare disease and recommend further diagnostic steps when the patient may be at risk.

Bingli provides digital questionnaires that empower patients to assess their eligibility for vaccinations.

» Pneumococcal infections caused by the bacterium Streptococcus pneumoniae are common in young children, but when infected, older adults in particular are at high risk of serious illness and death.

» Bingli has developed a digital solution to determine which patients are eligible for adult pneumococcal vaccination.

To increase the early detection of AF, Pfizer asked Bingli to develop a population screening tool that would allow to detect both symptomatic and asymptomatic patients.

» The objective of this project was twofold: validate the Bingli algorithms for the detection of symptomatic AF and build a screening questionnaire to detect asymptomatic patients at risk based on risk factors.

» A model was developed based on virtual clinical cases (vignettes): 1290 vignettes were created and presented to cardiologists for assessment. 

» The expert feedback was used to train the model and define the threshold of risk factors for a patient to be considered at risk of AF.

» This model is included is a short questionnaire that is send to an eligible population. Patients at risk can then be flagged to caregivers for confirmation of diagnosis.