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TAIMedImg DeepMets is a software device intended to assist trained medical professionals by providing initial object contours on axial T1-weighted contrast-enhanced (T1WI+C) brain magnetic resonance (MR) images to accelerate workflow for radiation therapy treatment planning.

TAIMedImg DeepMets is intended only for patients with known (imaging diagnosed) brain metastases (BM) when cancer cells spread from primary site to the brain. It is not intended to be used with images of other brain tumors or other body parts. The software is intended for use with BM lesions with a diameter of ≥ 10 mm.

TAIMedImg DeepMets uses an artificial intelligence algorithm to contour images and offers automated segmentation for Gross Tumor Volume (GTV) contours of brain metastases. The software is an adjunctive tool and not intended for replacing the users' current standard practice of manual contouring process. All automatic output generated by the software shall be thoroughly reviewed by a trained medical professional prior to delivering any therapy or treatment. The physician retains the ultimate responsibility for making the final diagnosis and treatment decision.

TAIMedImg DeepMets is intended to be used by medical professionals trained in the use of the device.

Only DICOM images of adult patients are considered valid input. DeepMets does not support DICOM images of patients that have one of the following exclusions:

• (i) presence of prior craniotomy
• (ii) patients with clinical imaging diagnosis of brain tumors other than BM
• (iii) Images with patient motion: excessive motion leading to artifacts that make the scan technically inadequate

Medical professionals must finalize (confirm or modify) the contours generated by TAIMedImg DeepMets, as necessary, using an external platform available at the facility that supports DICOM-RT viewing/editing functions, such as image visualization software and treatment planning system.

TAIMedImg DeepMets is a software application system intended for use in the contouring (segmentation) of brain magnetic resonance (MR) images. The device comprises an AI inference module and a DICOM Radiotherapy Structure Sets (RTSS, or RTSTRUCT) converter module.

The AI inference module consists of image preprocessing, deep learning neural networks, and postprocessing components, and is intended to contour brain metastasis on the axial T1-weighted contrast-enhanced (T1WI+C) MR images. It utilizes deep learning neural networks to generate contours and annotations for the diagnosed brain metastases.

The DICOM RTSS converter module converts the contours, annotations, along with metadata, into a standard DICOM-RTSTRUCT file, making it compatible with radiotherapy treatment planning systems.

Here's a breakdown of the acceptance criteria and study details for TAIMedImg DeepMets, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance

Note: "Deep learning" in the Source column indicates comparisons to similar FDA-cleared deep learning devices. "Estimated" indicates acceptance criteria were based on literature and clinical justification.

Study Information

2. Sample size used for the test set and the data provenance:

• Sample Size: 158 MRI scans from 158 patients, containing 289 measurable lesions (≥ 10 mm in diameter, as defined by RANO-BM criteria).
• Data Provenance: The test set was an independent U.S. dataset collected from 16 imaging facilities, acquired using scanners from GE, Philips, Siemens, and Toshiba. It was completely independent and not used in any stage of algorithm development. The data is retrospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: Three (3) clinically experienced radiologists/neuroradiologists.
• Qualifications: "Clinically experienced radiologists/neuroradiologists." Specific years of experience are not mentioned.

4. Adjudication method for the test set:

• Adjudication Method: Ground truth annotations were established based on consensus NRG/RTOG clinical guidelines by the three experts. This implies a consensual agreement among the three.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• The provided document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human reader improvement with AI assistance. The performance testing described is a standalone evaluation of the algorithm against expert-defined ground truth.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

• Yes, a standalone performance testing was conducted. The results in the table above reflect the algorithm's performance without human intervention after the initial contour generation.

7. The type of ground truth used:

• Expert Consensus: Ground truth annotations were manually established based on consensus NRG/RTOG clinical guidelines by three clinically experienced radiologists/neuroradiologists.

8. The sample size for the training set:

• Initial Training: 1,029 patients.
• Further Tuning: 559 patients.
• Total Training/Tuning Sample Size: 1,029 + 559 = 1,588 patients.

9. How the ground truth for the training set was established:

• The document states the initial training dataset was collected from a major medical center in Taiwan between 1993 and 2017. For the further tuning dataset, an additional dataset from a nationwide healthcare database (2018-2019) was used. However, the document does not explicitly describe how the ground truth for the training dataset was established (e.g., by experts, pathology, etc.). It only mentions that the model was "trained on a retrospective dataset...".

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