Search Results

The Presto Computed Tomography system is an x-ray imaging device that produces cross-sectional images of the body at different angles. The system reconstructs, processes, displays, and stores the collected images. The device output can provide an aid to diagnosis when used by a qualified physician.

The Presto is a multi-slice computed tomography system that uses x-ray data to produce cross-sectional images of the body at various angles. The Presto system uses "third generation" CT technology, where the x-ray tube and detector assemblies are mounted on a frame that rotates continuously around the patient using slip ring technology. The solid-state detector assembly design collects up to 4 slices of data simultaneously. The x-ray sub-system features a high frequency generator, xray tube, and collimation system that produces a fan beam x-ray output. The system can operate in a helical (spiral) scan mode where the patient table moves during scanning. As the x-ray tube/detector assembly rotates around the patient, data is collected at multiple angles. The collected data is then reconstructed into cross-sectional images by a high-specd reconstruction sub-system. The images are displayed on a computer workstation, stored, printed, and archived as required. The workstation is based on current PC technology using the Windows™ operating system. The Presto system consisting of a gantry, operator's workstation, patient table, high-frequency x-ray generator, and accessories.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Hitachi Presto Computed Tomography X-ray System:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state numerical acceptance criteria for specific performance metrics. Instead, it relies on a comparison to a predicate device. The performance is deemed acceptable because it is "comparable to the predicate device."

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a sample size for a "test set" in the context of clinical images or patient data. The evaluation described is purely non-clinical, comparing the physical and performance characteristics of the Presto system to its predicate device. Therefore, clinical data provenance is not applicable.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

This information is not applicable as the evaluation did not involve human interpretation of images or clinical ground truth establishment by experts. The study focused on technical measurements of the device's physical and performance characteristics.

4. Adjudication Method (for the test set)

This information is not applicable as there was no test set involving human assessment or a need for adjudication.

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

There was no MRMC comparative effectiveness study conducted or described. The device is a CT scanner, not an AI-assisted diagnostic tool in the sense of image analysis software. The evaluation is for the imaging system itself.

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

This concept is not directly applicable in the context of a CT scanner as the "algorithm" is the image reconstruction software integral to the device. The evaluation was of the entire system's performance, which includes the reconstruction algorithms, but it wasn't a separate "standalone algorithm performance" study as typically understood for AI/CAD devices. The non-clinical evaluations (dose profile, image noise, MTF, etc.) assess the output of the system including its algorithms.

7. The type of ground truth used

The "ground truth" for the non-clinical evaluations was based on established physical and engineering measurements and standards as stipulated in 21 CFR 1020.33(c). These involve objective measurements of physical parameters like x-ray output, spatial resolution, noise, and dose, rather than clinical outcomes or pathology. The performance of the Presto was then compared to these objective measurements and, more importantly, to the performance of the predicate device.

8. The sample size for the training set

This information is not applicable. The Presto is a CT scanner, not a machine learning model that requires a "training set" of data in the AI sense. The development and validation of such a system involve engineering design, component testing, and system-level performance verification, not data-driven training.

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

This information is not applicable since there was no training set in the context of a machine learning algorithm.

Ask a specific question about this device