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MIM software is used by trained medical professionals as a tool to aid in evaluation and information management of digital medical images. The medical image modalities include, but are not limited to, CT, MRI, CR, DX, MG, US, SPECT, PET and XA as supported by ACR/NEMA DICOM 3.0. MIM assists in the following indications:

• Receive, transmit, store, retrieve, display, print, and process medical images and DICOM objects.
• Create, display and print reports from medical images.
• Registration, fusion display, and review of medical images for diagnosis, treatment evaluation, and treatment planning.
• Evaluation of cardiac left ventricular function and perfusion, including left ventricular end-diastolic volume, end-systolic volume, and ejection fraction.
• Localization and definition of objects such as tumors and normal tissues in medical images.
• Creation, transformation, and modification of contours for applications including, but not limited to, quantitative analysis, aiding adaptive therapy, transferring contours to radiation therapy treatment planning systems, and archiving contours for patient follow-up and management.
• Quantitative and statistical analysis of PET/SPECT brain scans by comparing to other registered PET/SPECT brain scans.
• Planning and evaluation of permanent implant brachytherapy procedures.

When used for diagnostic purposes, the mobile thin client is not intended to replace a full workstation and should only be used when there is no access to a workstation.

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations. Images that are printed to film must be printed using a FDA-approved printer for the diagnosis of digital mammography images must be viewed on a display system that has been cleared by the FDA for the diagnosis of digital mammography images. The software is not to be used for mammography CAD.

MIM - Thin Client (mobile) is a software package designed for use in diagnostic imaging and oncology. It is a stand-alone package, which operates on both Windows and Mac computer systems and can be configured as a server for display and advanced visualization of medical image data on remote workstations or high-resolution mobile devices. Off-the-shelf software, such as Citrix, will provide connectivity between the MIM workstation, when acting as a server, and the remote display.

MIM aids the efficiency of medical professionals by providing various tools for display, registration and fusion of medical images from multiple modalities, to quickly create, transform, and modify contours for applications including, but not limited to, quantitative analysis, aiding adaptive therapy, transferring contours to radiation therapy treatment planning systems, and archiving contours for patient follow-up and management.

MIM functions as a medical image and information management system intended to receive, transmit, store, retrieve, display, print and process digital medical images, as well as create, display and print reports from those images. It also functions as a general-purpose brachytherapy planning system used for prospective and confirmation dose calculations for patients undergoing a course of brachytherapy using permanent implants of various radioisotopes.

The provided text is a 510(k) summary for the MIM – Thin Client (mobile) device. It describes the device's intended use, indications for use, and a brief summary of performance data. However, it does not contain detailed information about specific acceptance criteria or a comprehensive study that proves the device meets those criteria in a quantitative, statistically driven manner.

The document states: "Testing with MIM – Thin client (mobile) was performed by board certified radiologists. Results of these studies affirm the diagnostic image viewing capabilities of MIM – Thin Client (mobile) when used as indicated. The following devices were validated for diagnostic reading..." This is a qualitative statement about the outcome of testing, but it does not provide the details of the study.

Therefore, many of the requested details cannot be extracted from this document.

Here's an attempt to answer the questions based only on the provided text, highlighting what is missing:

1. A table of acceptance criteria and the reported device performance

The document does not provide a table of acceptance criteria with specific quantitative metrics. It broadly states: "In all cases, the software passed its performance requirements and met specifications." and "Results of these studies affirm the diagnostic image viewing capabilities of MIM – Thin Client (mobile) when used as indicated."

Acceptance Criteria (Extrapolated/Inferred - Not Explicitly Stated Quantitatively):

• Ability to perform diagnostic image viewing.
• Software passes performance requirements.
• Software meets specifications.

Reported Device Performance (Qualitative):

• Affirms diagnostic image viewing capabilities.
• Software passed performance requirements.
• Software met specifications.
• Validated for diagnostic reading on specific mobile devices (Apple iPad - A1458, Kindle Fire HDX 8.9, Samsung Galaxy NotePRO - SM-P905V, Windows Surface - 1516).

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size: Not specified. The document only mentions "studies."
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Number of Experts: Not specified.
• Qualifications of Experts: "Board certified radiologists." No detail on their years of experience or subspecialty.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not specified.

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 document implies that human readers (radiologists) were involved in testing the "diagnostic image viewing capabilities." However, it does not describe an MRMC study, nor does it compare human readers' performance with vs. without AI assistance (as this is an image viewing and management device, not an AI diagnostic tool in the sense of CADx).
• No effect size is mentioned.

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

• The device is described as a "software package designed for use in diagnostic imaging and oncology... aids the efficiency of medical professionals."
• It is not an AI algorithm intended to provide diagnostic interpretations without human involvement. Its primary function is image display, management, and processing for medical professionals. Therefore, a standalone performance study in the context of an "algorithm only" would not be applicable in the typical sense of an AI diagnostic tool.
• The performance data focuses on the "diagnostic image viewing capabilities" with human involvement, and software passing its performance requirements.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Not specified. The document only mentions testing was performed by "board certified radiologists" to "affirm the diagnostic image viewing capabilities," implying subjective human assessment, but the method for establishing a reference standard (ground truth) is not detailed.

8. The sample size for the training set

• This information is not applicable for this device as it's not described as a machine learning (AI) device that undergoes a "training" phase in the typical sense. It is a medical image and information management system.

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

• Not applicable (see point 8).

Summary of missing information:

The 510(k) summary provides a high-level overview but lacks granular details on the study methodology, specific quantitative acceptance criteria, test set characteristics (size, provenance), expert panel specifics, and adjudication methods. This is common for 510(k) summaries, which aim to demonstrate substantial equivalence rather than provide a detailed scientific publication of clinical trial results. For an AI/ML-driven device, significantly more detail in these categories would be expected.

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