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510(k) Data Aggregation
(223 days)
The SONOCA 180/185 Wound Care System is an instrument intended for selected ultrasound dissection and fragmentation of tissue, wound debridement (acute and chronic wounds, burns, diseased or necrotic tissue) and cleansing irrigation of the site for removal of debris, exudates, fragments, and other matter.
Ultrasonic-Assisted Wound Treatment using the Sonoca 180 / 185 applies low-frequency power ultrasound in conjunction with an irrigation solution via a moving receptacle directly to the wound tissue. An ultrasonic generator transfers electric energy to a receptacle where a high-precision piezoelectric crystal system (PZT transducer) transforms this energy into mechanic vibrations: 25,000 vibrations / second (= 25 kHz). The receptacle is continuously moved over the wound surface. This allows gentle loosening of necrotic tissue and fibrin layers and flushes them away with the irrigation solution (0.9 % NaCl or Ringer solution). The liquid is used for transmitting the ultrasound as well as for wound irrigation. Cavitation is the underlying mode of action of ultrasound: the formation and disintegration of cavities (bubbles) in liquids due to pressure fluctuations, i.e. formation and subsequent implosion of these bubbles causes turbulences and currents on the wound surface, which help to loosen necrotic tissue and fibrin layers.
Here's an analysis of the provided text regarding the acceptance criteria and study for the Soring GmbH SONOCA 180/185 Wound Care System:
The provided document (K072904) is a 510(k) summary, which focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than presenting a detailed clinical study with specific acceptance criteria related to a new performance claim. Therefore, the information typically found in acceptance criteria tables and detailed study designs for performance (e.g., accuracy, sensitivity, specificity) is largely absent.
Instead, the "acceptance criteria" for this type of submission are primarily related to compliance with recognized standards and demonstrating that the device is substantially equivalent to predicate devices in terms of intended use, technological characteristics, and safety and effectiveness.
Here's a breakdown based on your requested information:
Acceptance Criteria and Reported Device Performance
1. A table of acceptance criteria and the reported device performance
Acceptance Criteria Category | Specific Criteria / Standard | Reported Device Performance (Compliance Statement) |
---|---|---|
Safety and Electrical Standards | IEC 950 - Safety of Information Technology Equipment | Complies |
CISPR 22, class A - Electromagnetic Compatibility | Complies | |
IEC-801-2 - Electromagnetic Compatibility | Complies | |
IEC-801-3 - Electromagnetic Compatibility | Complies | |
IEEE 1003.1 - General Electrical Safety for medical devices | Complies | |
IEC 601-1 - Electrical Safety for medical devices using RF-power | Complies | |
IEC 601-2-2 - Ultrasonic surgical devices | Complies | |
DIN EN 61847 | Complies | |
Substantial Equivalence | Similar general function, intended use, and technological characteristics to predicate devices. | Stated as "substantially equivalent to the predicate device" in areas of general function and intended use. Differences "will not affect safety or efficacy." |
Risk Assessment | Device does not control any life-sustaining functions or services (implied safety characteristic) | Stated: "Similar to the predicate devices, the Sonoca 180/185 does not control any life sustaining functions or services." |
Note on "Acceptance Criteria" in this context: For a 510(k) of this nature, the "acceptance criteria" are not quantitative performance metrics (like sensitivity/specificity targets) but rather adherence to consensus standards and a qualitative assessment of substantial equivalence. The document doesn't provide specific numerical targets for clinical efficacy or safety beyond compliance with general electrical and medical device safety standards.
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
This 510(k) does not describe a clinical study with a "test set" in the traditional sense of evaluating efficacy on patient data. The "performance data" section refers to compliance with safety and electrical standards, not clinical performance data on a patient cohort. Therefore, no information on sample size, data provenance, or study design (retrospective/prospective) is provided related to clinical performance. The evaluation is primarily based on engineering and design controls.
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)
Not applicable. As there is no clinical "test set" and a ground truth establishment process described, this information is not provided. The submission relies on established engineering standards.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
Not applicable. No clinical test set or adjudication process is described.
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
Not applicable. This device is an ultrasonic wound care system, not an imaging or diagnostic AI-assisted device. Therefore, no MRMC study or AI assistance is mentioned.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
Not applicable. This is a physical medical device (ultrasonic wound cleaner), not a software algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable in the context of clinical performance evaluation. The "ground truth" for this submission would be the established requirements within the referenced safety and electromagnetic compatibility standards (e.g., electrical parameters, radiation limits, etc.).
8. The sample size for the training set
Not applicable. This is a hardware medical device, not a machine learning algorithm requiring a "training set."
9. How the ground truth for the training set was established
Not applicable. (See answer to #8).
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