AI/ML Development Services

Innolitics has deep expertise in image processing and algorithm development. Our algorithms have been deployed in several FDA cleared applications and we can build your algorithm as part of a new device or to augment an existing device. Our competencies include:

  • Deep neural network based algorithms for image classification and segmentation
  • Natural language processing and textual data mining
  • Time series signal processing and anomaly detection
  • Hybrid traditional and neural network based algorithms
  • Hand-crafted heuristics based algorithms
  • Analyzing images and text contained in medical file formats such as DICOM, NIfTI, and HL7
  • Writing algorithm results in PDF encapsulated HL7, PDF encapsulated DICOM, DICOM structured reporting, and DICOM secondary capture

We provide the following services:

Engineering Consulting

It can be overwhelming to create an algorithm from scratch. We have been there. We can help you answer some common questions:


  • What kind of algorithm should I use?
  • Is my problem suitable for machine learning?
  • Do I have enough data for a machine learning approach?
  • How should I annotate my data for a machine learning based approach?
  • Given a budget and problem, what kind of accuracy can I expect?
  • How can I decompose my problem into chunks that are most likely to be solvable?
  • Should I use deep learning or a more traditional machine learning approach?


  • How much will it cost and how long will it take?
  • How do I source data? How about annotations?
  • Which data annotation tool should I use?
  • Should I train neural networks on the cloud or buy my own servers?


  • How can I embed my algorithm into an existing product?
  • How do I deploy my algorithm in the cloud?

Regulatory Consulting

We are well versed with the FDA’s stance on AI and ML. We can help you design compliant SOPs for data collection, data annotation, algorithm design, development, verification and validation.

Here are some common questions we can answer:

  • Does my algorithm qualify as CADe (computer aided detection) or CADx (computer aided diagnosis)?
  • What is the best strategy to get FDA clearance?
  • How can I ensure my data annotation protocol and algorithm development plan will be cleared by the FDA?

Data Sourcing

We can help you find data from a variety of places. Some common sources include:

  • Publicly available datasets such as the TCIA
  • Our multi-institutional data sourcing partners
  • Facilitate anonymization and cloud upload of data from your medical practice or university

Data Annotation

We can help you find qualified data annotation specialists for your data. Tap into our network of clinicians and technicians to annotate your data as cost effectively as possible.

We can also help you devise a data annotation strategy that minimizes the time that trained experts need to spend. For example, a technician can outline the border of a tumor—a time consuming task— but it takes a trained radiologist to find the correct slice. Our data annotation protocol could first involve trained radiologists to find the key slices and then hand it over to technicians to finish the rest.

End-to-End Algorithm Design and Development

You give us annotated data and we give you an algorithm that meets your accuracy and performance targets. Here are some specifics about our process:

  1. Discovery phase: We work with you to gather requirements and understand the business value of the algorithm.
  2. Prototype phase: We deliver the first version of the algorithm as fast as possible. This allows you the chance to verify we are headed in the direction you anticipate and allows us to prove—or disprove—feasibility early on in the process.
  3. Iteration phase: Algorithms development can have twists and turns. Sometimes we find edge cases that require a change in direction. Sometimes we find certain types of data is under-represented in the dataset. Sometimes the algorithm finds human errors in the dataset—an encourging sign. We provide you with weekly updates about the state of development until the performance targets are met.
  4. Design transfer: We can help integrate the algorithm into your product or work with your engineers to do so. Want us to develop your entire product and handle the 510(k) submission for you instead? Check out our end-to-end startup package.

We use the following tools in our toolbox to accomplish the above:

  • Deep neural network frameworks: TensorFlow and Pytorch
  • Deep neural network toolbox: Transfer learning, U-Net, data augmentation, Auto-ML, self supervised learning, Siamese networks, GANs
  • Image processing frameworks: ITK, VTK, PIL, Numpy, SciPy, OpenCV
  • Image processing toolbox: Convolutional filters, morphological operations, spectral analysis, Hough transform, level sets
  • Cloud technologies: AWS S3, Athena, Step functions, Lambda, Batch, EC2, ECR, Comprehend, SageMaker
  • Other tools: Jupyter notebooks, Docker with GPU support, Tesseract OCR, 3D Slicer


What if I am not finished annotating my data?

We can train the model as your team annotates data. You can see the algorithm performance improve over time. We can repeat the annotation and training cycle until we have met our performance targets.

How much does it cost to develop an algorithm?

There are several factors that contibute to the overall cost of an algorithm. The two important ones are the inherent complexity of the algorithm and the performance target. As a rule of thumb, you are in good shape if a toddler can be taught to solve your problem in three seconds or less. For example, a toddler need not have a medical degree to properly segment a brain tumor. If this is not true, then try to break up your problem into smaller three second chunks that a toddler could solve. Each three second chunk will need to be developed as a separate algorithm. The cost scales exponentially with each chunk. For example, a simple task requiring just one chunk may cost $10k but a complex task requiring five chunks could cost $1M. Complicated tasks requiring an expert to combine multiple sources of knowledge and life experiences is not solvable with the current state of AI.

As a rule of thumb, the cost of the algorithm increase exponentially the more 9’s you add to the performance target. For example, if 90% accuracy costs $10k, then 99% could cost $100k, and 99.9% could cost $1M.

Cloud machine learning training is expensive. Do you provide an alternative?

Yes. We have powerful machine learning servers on site that we use for your project—free of charge.

Hosting machine learning algorithms on the cloud is expensive. Do you provide an alternative?

For a small monthly fee, we can host your prototype for non-production use cases such as investor demos, but we recommend moving to the cloud once you get into the production phase.

Can you rescue a struggling project?

We can pick up where another firm left off. Each case is unique so please schedule an initial consultation.

When do you use a traditional algorithm vs a deep neural network?

Deep neural networks have disadvantages. They are difficult to debug, fail in unpredictable ways, and can cause problems with FDA submissions. Because of this, we prefer to use traditional algorithmic approaches when possible. Some problems, especially image classification, are trivial for the human eye to solve but difficult to describe in a way that a computer can understand. A deep neural network is useful here because we do not need to teach a computer anything, it just learns from many examples. The disadvantage is we do not know exactly what the computer is learning.

Do you have an algorithm design and development process?

We have a process that we can customize for your project. See some considerations here.

Let's Talk

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