Better Diagnostics Caries Assist (BDCA) Version 1.0

K241725

Better Diagnostics AI Corp · cleared 2025-03-11 · product code MYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“Better Diagnostics Caries Assist is a radiological, automated, concurrent read, CADe software intended to identify and localize carious lesions on bitewings and periapical radiographs acquired from patients aged 18 years or older.”
AlgorithmComputer Vision Models (CV Models) hosted on a cloud computing platform, responsible for image processing, providing a binary indication for carious findings and outputting bounding box coordinates. Comprises Pre-Processing, Core, and Post-Processing Modules.
source quote (p.7)
“Computer Vision Models (CV Models): These models are hosted on a cloud computing platform and are responsible for image processing. They provide a binary indication to determine the presence or absence of carious findings. If carious findings are detected, the software will output the coordinates of the bounding boxes for each finding. If no carious lesions are found, the output will not contain any bounding boxes and will have a message stating "No Suspected: Caries Detected" AI models have three parts: Pre-Processing Module: Standardization of image to specific height and width to maintain consistency for AI model. Finds out the type of image including IOPA, Bitewings or other types. BDCA v1.0 can only process Bitewings and IOPA images for patients over age 18. All other types of images will be rejected. Core Module: This module provides carious lesion annotations and co-ordinates to draw bounding boxes. Post-Processing Module: includes cleanup process to remove outliers/incorrect annotations from the images.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.14)
“identification and mitigation of device-related hazards via cybersecurity and risk management, labeling validation, human factors testing, standalone and clinical performance testing : Additionally, the software validation activities were performed in accordance with the FDA Guidance documents, "Content of Premarket Submissions for Device Software Functions" and "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions”.”

Validation studies (2)

Standalone

n=1,298 images

endpoints: sensitivity; specificity

standards: IEC 62304 Edition 1.1 2015-06, IEC 62366-1:2015, ISO 14971 Third Edition 2019-12, ISO 15223-1:2021

Reader study (MRMC)

n=328 images

endpoints: AFROC; sensitivity; specificity

Reported performance (20 observations)

sensitivity0.892CI [86.15%, 92.13%]
source quote (p.15)
“BW Surface Level: The BDCA achieved a sensitivity of 89.2% with an adjusted 95% CI of [86.15%, 92.13%], and a specificity of 99.5% with a CI of [99.32%, 99.57%].”
specificity0.995CI [99.32%, 99.57%]
source quote (p.15)
“BW Surface Level: The BDCA achieved a sensitivity of 89.2% with an adjusted 95% CI of [86.15%, 92.13%], and a specificity of 99.5% with a CI of [99.32%, 99.57%].”
aurocas written: “auc”0.848
source quote (p.17)
“The results from the aided groups, displaying AUCs of 0.848 for BW and 0.845 for IOPA images, significantly surpass those of the unaided groups, which recorded AUCs of 0.806 and 0.807, respectively.”
sensitivityas written: “IOPA Surface Level Sensitivity”0.882CI [85.27%, 90.78%]
source quote (p.15)
“IOPA Surface Level: The device reached a sensitivity of 88.2% with a CI of [85.27%, 90.78%] and a specificity of 99.1% with a CI of [98.88%, 99.31%], exceeding the goals of 0.76 and 0.95 respectively.”
specificityas written: “IOPA Surface Level Specificity”0.991CI [98.88%, 99.31%]
source quote (p.15)
“IOPA Surface Level: The device reached a sensitivity of 88.2% with a CI of [85.27%, 90.78%] and a specificity of 99.1% with a CI of [98.88%, 99.31%], exceeding the goals of 0.76 and 0.95 respectively.”
sensitivityas written: “BW Image Level Sensitivity (Conservative)”0.81CI [76.15%, 85.18%]
source quote (p.16)
“BW Image Level: Sensitivity under conservative conditions was reported at 81.0%, with a CI of [76.15%, 85.18%], and under optimistic conditions, it improved to 91.9%, with a CI of [88.33%, 94.71%].”
sensitivityas written: “BW Image Level Sensitivity (Optimistic)”0.919CI [88.33%, 94.71%]
source quote (p.16)
“BW Image Level: Sensitivity under conservative conditions was reported at 81.0%, with a CI of [76.15%, 85.18%], and under optimistic conditions, it improved to 91.9%, with a CI of [88.33%, 94.71%].”
specificityas written: “BW Image Level Specificity”0.984CI [96.20%, 99.44%]
source quote (p.16)
“Specificity remained consistent at 98.4% across definitions with a CI of [96.20%, 99.44%].”
sensitivityas written: “IOPA Image Level Sensitivity (Conservative)”0.831CI [78.87%, 86.80%]
source quote (p.16)
“IOPA Image Level: Sensitivity was remarkably high at 83.1% with a CI of [78.87%, 86.80%] under conservative conditions, and under optimistic conditions, it improved to 91.8%, with a CI of [88.54%, 94.42%], substantially exceeding the target goal of 0.75.”
sensitivityas written: “IOPA Image Level Sensitivity (Optimistic)”0.918CI [88.54%, 94.42%]
source quote (p.16)
“IOPA Image Level: Sensitivity was remarkably high at 83.1% with a CI of [78.87%, 86.80%] under conservative conditions, and under optimistic conditions, it improved to 91.8%, with a CI of [88.54%, 94.42%], substantially exceeding the target goal of 0.75.”
specificityas written: “IOPA Image Level Specificity”0.984CI [96.20%, 99.44%]
source quote (p.16)
“Specificity was also impressive at 98.4% with a CI of [96.20%, 99.44%].”
aurocas written: “MRMC Aided IOPA AUC”0.845
source quote (p.17)
“The results from the aided groups, displaying AUCs of 0.848 for BW and 0.845 for IOPA images, significantly surpass those of the unaided groups, which recorded AUCs of 0.806 and 0.807, respectively.”
aurocas written: “MRMC Unaided BW AUC”0.806
source quote (p.17)
“The results from the aided groups, displaying AUCs of 0.848 for BW and 0.845 for IOPA images, significantly surpass those of the unaided groups, which recorded AUCs of 0.806 and 0.807, respectively.”
aurocas written: “MRMC Unaided IOPA AUC”0.807
source quote (p.17)
“The results from the aided groups, displaying AUCs of 0.848 for BW and 0.845 for IOPA images, significantly surpass those of the unaided groups, which recorded AUCs of 0.806 and 0.807, respectively.”
sensitivityas written: “MRMC Aided BW Sensitivity (Optimistic)”0.89
source quote (p.17)
“Specifically, for BW images, the sensitivity improved from 0.857 to 0.890, and for IOPA images from 0.761 to 0.809 under aided conditions.”
sensitivityas written: “MRMC Aided IOPA Sensitivity (Optimistic)”0.809
source quote (p.17)
“Specifically, for BW images, the sensitivity improved from 0.857 to 0.890, and for IOPA images from 0.761 to 0.809 under aided conditions.”
sensitivityas written: “MRMC Aided BW Sensitivity (Surface Level)”0.763
source quote (p.18)
“For BW Images, the overall sensitivity under aided conditions is significantly higher (0.763) compared to unaided conditions (0.707), with a statistically significant mean increase of 0.056.”
sensitivityas written: “MRMC Aided IOPA Sensitivity (Surface Level)”0.746
source quote (p.18)
“For IOPA Images, aided conditions show a higher overall sensitivity (0.746) than unaided conditions (0.691), with a significant mean increase of 0.055.”
specificityas written: “MRMC Aided BW Specificity (Surface Level)”0.98
source quote (p.18)
“For BW Images, the overall specificity under aided conditions shows an increase to 0.980 from 0.974 in unaided conditions.”
specificityas written: “MRMC Aided IOPA Specificity (Surface Level)”0.983
source quote (p.19)
“Similarly for IOPA Images, the overall specificity under aided conditions is slightly improved at 0.983 compared to 0.979 in unaided conditions.”

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

0
recalls in product code, 24mo
0
MAUDE reports in code, 12mo
-100%
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Radiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K241725