We are a translational research group that creates machine learning models to help guide clinicians

or

We use

Machine learning and Artificial intelligence

to help clinicians in the Real World.

Data Augmentation

To ensure that our models are robust and can tolerate the variety in real world data, we AUGMENT our training datasets through random transformations.

Training and Evaluation

After the model has been trained on large datasets, we evaluate our models using data that have NOT been seen by the model before.

Deployment

We deploy our models on our website using powerful TensorFlow.JS technology.

Security

Our models run locally on your machine to ensure privacy and security of your data. Data is NOT sent to a server for inference.

TM Classifier

We created a Tympanic Membrane image classifier based on Deep Learning that is capable of classifying given images into multiple pathologies.

Try our TM Classifier

0.90

We achieved an AUC of 0.90 for differentiating NORMAL and ABNORMAL tympanic membrane images on a dataset that has never been seen by the model before.

0.91

For multiclass classification, we achieved an AUC of 0.91. Five different pathologies were included: Normal, Acute otitis media, Chronic suppurative otitis media, Otitis externa, and Cerumen.

Thyroidectomy Complication Classifier

We created a Thyroidectomy classifier based on Deep Learning that is capable of classifying based on set criteria.

Try our Thyroidectomy Classifier

0.90

We achieved an AUC of 0.90 for differentiating NORMAL and ABNORMAL tympanic membrane images on a dataset that has never been seen by the model before.

0.91

For multiclass classification, we achieved an AUC of 0.91. Five different pathologies were included: Normal, Acute otitis media, Chronic suppurative otitis media, Otitis externa, and Cerumen.