Microbleed Count Tool
Actual distinct candidate inventory of independently qualified cerebral microbleeds, retaining excluded and unknown evidence. Clinical/image assignment external, not automatic detection or etiology/prognosis.
Description
A microbleed count depends on independently qualified lesion candidates and imaging criteria.
A microbleed count depends on independently qualified lesion candidates and imaging criteria.1
Unknown candidates should remain distinct from confirmed and excluded lesions.
When to use Microbleed Count Tool
Use this tool to calculate or review the supplied observations under the stated rules. Establish the assessment or recording protocol, units, instrument version and independently applicable reference or calibration.
How to use Microbleed Count Tool and understand its results
- Establish the assessment or recording protocol, units, instrument version and independently applicable reference or calibration.
- Enter callerCandidateLesionIds, independentlyQualifiedLesionFlags, lesionEvidenceKnownFlags, using the field meanings and rules below, then run the tool.
- Read result.knownQualifiedLesionCount, result.knownQualifiedLesionIds, result.knownExcludedLesionIds, result.unknownLesionIds. Use the units and sign conventions below. Review unresolved records and any possible bounds separately from known results.
- Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| callerCandidateLesionIds | Caller candidate lesion ids | list of string | Required |
| independentlyQualifiedLesionFlags | Independently qualified lesion flags | list of number | Required |
| lesionEvidenceKnownFlags | Lesion evidence known flags; binary 1 means the matched observation is known, 0 means unknown | list of number | Required |
Require unique nonblank candidate IDs and aligned binary qualification and known flags. Count and list only candidates with known=1 and qualification=1. Return known excluded IDs where qualification=0 and unknown IDs independently of their placeholder classification. All unknown produces known count zero without asserting no lesions.
qualified={id_i:known_i=1 and qualification_i=1}; excluded={id_i:known_i=1 and qualification_i=0}; unknown={id_i:known_i=0} Use the matching field units and the exact boundary, ordering and reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Structured result; its nested fields below contain the actual calculation or selected reference | object |
| result.knownQualifiedLesionCount | Number of candidate lesions independently marked both known and qualifying | number |
| result.knownQualifiedLesionIds | IDs marked known and qualifying | list |
| result.knownExcludedLesionIds | IDs marked known and not qualifying | list |
| result.unknownLesionIds | Unresolved candidate IDs; a placeholder qualification flag does not resolve them | list |
{
"input": {
"callerCandidateLesionIds": [
"a",
"b",
"c"
],
"independentlyQualifiedLesionFlags": [
1,
0,
1
],
"lesionEvidenceKnownFlags": [
1,
1,
0
]
},
"output": {
"result": {
"knownQualifiedLesionCount": 1,
"knownQualifiedLesionIds": [
"a"
],
"knownExcludedLesionIds": [
"b"
],
"unknownLesionIds": [
"c"
]
}
}
}Limitations and assumptions
Actual acquisition, assessment protocol, instrument version and calibration determine applicability. The tool does not examine a person, classify images, administer a questionnaire or choose diagnostic cutoffs.
Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries; ordinary string lists allow at most 128 strings of 128 UTF-16 units each; numeric matrices allow at most 128 rows and 128 columns, subject to narrower requirements above. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related step, use Perivascular Space Rating Calculator: Perivascular-space ratings belong to an applicable anatomical and imaging reference. For a complementary comparison, use Lacune Count Tool: Lacune counting requires independently established imaging classification.
References
-
Microbleed Count Tool — tool.io
Similar or alternative tools
- Perivascular Space Rating Calculator
Perivascular-space ratings belong to an applicable anatomical and imaging reference.
- Lacune Count Tool
Lacune counting requires independently established imaging classification.