---
AI Summary: "\\- Built robust web app (Svelte UI, Django backend) with TensorFlow\
  \ AI models.  \n- Trained deep‑learning segmentation models for dental anatomy,\
  \ caries and periapical lesions.  \n- Implemented multi‑image upload, analysis,\
  \ secure login, patient portal, and exam approval system.  \n- Achieved 510(k) clearance\
  \ (May2025) improving detection performance."
Anonymous: false
Assignee:
- Matt Hancock
Last Edited Time: '2026-05-09T20:24:00+00:00'
case_study_510ks:
- 119bd5b7-a754-80b1-84b6-c974750ab5d8
client_logo:
- 182bd5b7-a754-8031-9af4-f3e29d640f4d
client_name: Cube Click, Inc.
date: '2024-11-28'
featured: true
medical_panel: Dental
name: Dental CADe Concept to 510(k). We built the app, trained the AI, ran the MRMC
  study, cleared the FDA.
services:
- 30311ab9-66a7-48a7-9dff-2927e1468480
- 9dc4f55a-b5cb-419f-8bae-5e11d6b11fee
summary: We built the app, trained the AI, ran the MRMC study, and cleared FDA.
tags:
- Web-App
- AI/ML
- Image Processing
- 510k
testimonials:
- 14abd5b7-a754-8075-8361-da11fc579a8c
- 14dbd5b7-a754-8044-81dd-d388a24b77ab
---

### The Problem

Dentists face significant challenges in accurately detecting caries (cavities), periodontal disease (gum disease), and periapical radiolucencies in X-ray images. Traditional diagnostic methods often lead to missed or delayed diagnoses, which can result in more severe dental issues for patients. These undetected problems may progress, potentially requiring more invasive and costly treatments in the future. The need for a more reliable and efficient diagnostic tool has been a pressing concern in the dental community, as it directly impacts patient care and long-term oral health outcomes.

### Highlights

- Our engagement with Cube Click went from concept to a fully engineered web application. **510(k) cleared May 2025 (K242437)**
- The application, Smile Dx, significantly improves the performance of detecting caries (cavities) and periapical radiolucencies for most categories of patients and imaging equipment.

### What We Did

<figure>
  <img src="/img/portfolio/Dental_CADe_Concept_to_510k._We_built_the_app_trained_the_AI_ran_the_MRMC_study_cleared_the_FDA.-145bd5b7a75480d0ace5edd200710595.png">
  <figcaption>
    The Smile Dx application shows boundaries of normal tooth anatomy in
    X-rays. Here shown are jaw bone, enamel, dentin, nerves, and pulp.
  </figcaption>
</figure>

<figure>
  <img src="/img/portfolio/Dental_CADe_Concept_to_510k._We_built_the_app_trained_the_AI_ran_the_MRMC_study_cleared_the_FDA.-145bd5b7a7548028a4d3c1d297bdab42.png">
  <figcaption>
    In addition to segmenting normal anatomy, the Smile Dx application
    identifies and segments caries (cavities).
  </figcaption>
</figure>

<figure>
  <img src="/img/portfolio/Dental_CADe_Concept_to_510k._We_built_the_app_trained_the_AI_ran_the_MRMC_study_cleared_the_FDA.-145bd5b7a754809d835ecfab96fb0a8b.png">
  <figcaption>
    In addition to segmenting normal anatomy, the Smile Dx application
    identifies and segments Periapical radiolucencies (PARLs).
  </figcaption>
</figure>

Cube Click developed Smile Dx, an innovative AI-powered dental diagnostic tool designed to assist dentists in identifying dental caries and periapical radiolucencies. Here\'s what we accomplished:

- Built a robust web application using modern frameworks for both frontend and backend development
- Implemented and trained deep-learning models for the automatic segmentation of dental anatomy in radiographs
- Developed a user-friendly interface allowing dentists to upload, view, and analyze multiple dental X-rays simultaneously
- Integrated advanced, custom image-processing techniques to enhance the visibility of dental structures and potential issues
- Created a secure login system with tiered access privileges based on subscription types
- Implemented an exam approval system and a patient portal for viewing approved results
- Ensured compliance with regulatory requirements, including comprehensive documentation and risk management

### Key Technologies

- Svelte-based web UI
- Django-based web server
- Tensorflow-based ML models

### Timeline

- **July 2021:** Project started. Proof of concept developed, including initial model training with a small dataset and basic frontend functionality. Demo deployed using ngrok.
- **September 2021:** Work on bone segmentation. Exploration of different approaches for handling various image types and edge cases.
- **January 2022:** Updated model training with additional data. Generation of performance reports.
- **March 2022:** SMILE Dx prototype completed
- **June 2022:** Refinement of FDA pre-submission documents. Addressing security considerations and penetration testing.
- **July 2022:** Completion of verification and validation testing. Finalization of regulatory documents and user manual.
- **June 2023:** FDA pre-submission
- **December 2023:** FDA standalone performance testing completed
- **March 2024:** FDA multi reader multi case (MRMC) study completed for CADe indication
- **August 2024:** 510(k) submitted
- **October 2024:** StartEngine crowdfunding kickoff. [Click here to invest.](https://www.startengine.com/offering/cubeclick)

![](/img/portfolio/Dental_CADe_Concept_to_510k._We_built_the_app_trained_the_AI_ran_the_MRMC_study_cleared_the_FDA.-14dbd5b7a75480ca9a48d3859a760d35.png)