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AI100 with Shonit™ is a cell locating device intended for in-vitro diagnostic use in clinical laboratories.

A1100 with Shonit™ is intended for differential count of White Blood Cells (WBC), characterization of Red Blood Cells (RBC) morphology and Platelet morphology. It automatically locates blood cells on peripheral blood smears and presents images of the blood cells for review.

A skilled operator, trained in the use of the device and in the review of blood cells, identifies each cell according to type.

The AI100 with Shonit™ device consists of a high-resolution microscope with LED illumination, and compute parts such as the motherboard, CPU, RAM, Wi-Fi dongle, SSD containing AI100 with Shonit™ software, motorized XYZ stage, a camera with firmware, PCB and its firmware for driving motor and LED, SMPS, power supply and a casing. It is capable of handling one Peripheral Blood Smear (PBS) slide at a time.

Software plays an intrinsic role in the A1100 with Shonit™ device, and the combination of hardware and software works together for the device to achieve its intended use. The main functions of the software can be summarized as follows:

• Allow the user to set up the device and perform imaging of a PBS slide. ●
• Control the hardware components (Camera, LEDs, Stages, etc) to take images of a . PBS slide.
• Store and manage images and other data corresponding to the PBS slide and present them to the user.
• Analyze images and allows user to identify components in the images and create a ● report for review.
• Allow the user to finalize, download and print a report.

Here's a breakdown of the acceptance criteria and study details for the AI100 with Shonit™ device, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly list a table of "acceptance criteria" alongside "reported device performance." Instead, it presents the results of various studies and states that "All tests met acceptance criteria." We can infer the acceptance criteria from the context of these results.

Here's an inferred table based on the "Method Comparison Study" section, which compares the device to manual microscopy:

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

• Sample Size for Method Comparison Study: A total of 882 samples were collected and analyzed.
  • 298 normal samples
  • 584 abnormal samples
• Data Provenance:
  • Country of Origin: Not explicitly stated, but the submitter is SigTuple Technologies Pvt. Ltd. from Bangalore, Karnataka, India. The regulatory consulting firm is US-based. The clinical study was conducted across four sites, implying a multi-site study.
  • Retrospective or Prospective: Not explicitly stated, but the phrasing "samples were collected and analyzed" suggests a prospective collection for the purpose of the study.

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

• Number of Experts: "two medical reviewers at each site" for the method comparison study. Since there were four sites, this implies a total of 8 medical reviewers.
• Qualifications of Experts: They were described as "trained qualified reviewers" and "skilled operator, trained in the use of the device and in the review of blood cells." The document specifies that the ground truth review was done by "performing manual microscopy," indicating expertise in manual blood smear analysis.

4. Adjudication Method for the Test Set

• The document states that the "stained slides were read by two medical reviewers at each site both on the AI100 with Shonit™ device and manual microscope (reference method)."
• It does not explicitly describe an adjudication method (e.g., 2+1, 3+1) if the two reviewers disagreed on the ground truth. It seems the comparison was direct, with both reviewers' manual microscopy results forming part of the "reference method."

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• A type of MRMC study was performed in the "Method Comparison Study" section, where "two medical reviewers at each site" read the slides. However, this study was primarily designed to compare the device's output (with human verification) to the manual microscopy method (human reference).
• The study design directly compares the device-assisted reading (where the human operator reviews and verifies the AI's suggestions) against the manual microscopy reference method. It is not designed to quantify the effect size of how much human readers improve with AI vs. without AI assistance for the same human reader. The human readers are presented with the AI's suggestions in one arm and perform traditional manual microscopy in the other.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• The document mentions "pre-classified results suggested by the device" and "pre-characterized results suggested by the device" in the repeatability and reproducibility studies (Sections 5.11 and 5.12). This indicates that the algorithm's raw classifications were evaluated in these analytical performance studies.
• The "principle of operation" also states: "On each FOV image, image processing is applied to extract and classify WBCs, RBCs, and Platelets."
• However, the clinical performance (method comparison study in Section 5.13) which leads to the substantial equivalence determination, is based on a human-in-the-loop workflow: "A skilled operator, trained in the use of the device and in the review of blood cells, identifies and classifies each cell according to type." and "The device then allows the user to review the identified and classified cells... The user may re-classify cells and add impressions as they deem fit and approve the report."
• Therefore, while the algorithm's internal performance was evaluated analytically, the regulatory submission for substantial equivalence focuses on the device's performance as a human-in-the-loop system, not a standalone AI.

7. The Type of Ground Truth Used

• The ground truth used for the clinical performance study (method comparison) was expert consensus / manual microscopy by skilled operators. Specifically, slides were "read by two medical reviewers at each site both on the AI100 with Shonit™ device and manual microscope (reference method)." The "reference method" from manual microscopy served as the ground truth.

8. The Sample Size for the Training Set

• The document does not explicitly state the sample size used for the training set of the AI. The discussions focus on analytical and clinical validation studies for regulatory clearance.

9. How the Ground Truth for the Training Set Was Established

• The document does not explicitly describe how the ground truth for the training set was established. It mentions the use of "neural network of convolutional type" which implies deep learning, but the specifics of its training data and ground truth labeling are not detailed in this regulatory summary.

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