Black Hole Count Tool

Count actual separately assessed distinctcandidateT1hypointenselesion eligible flags, not classify MRI intensities or identify astronomical black holes. Actual independently matched observations/units and protocol, not diagnosis, causal mechanism, clinical decision or validated individual prediction.

Description

In this MRI context, a black-hole count concerns independently qualified T1-hypointense lesion candidates.

In this MRI context, a black-hole count concerns independently qualified T1-hypointense lesion candidates.^[1]

This tool sums supplied binary eligibility flags without recognizing lesions from image intensities.

When to use Black Hole Count Tool

This tool sums supplied binary eligibility flags without recognizing lesions from image intensities.

How to use Black Hole Count Tool and how it works

Prepare independently qualified t1 black hole eligibility flags, using the input definitions and matching units below.
Enter the required fields and run Black Hole Count Tool.
Read result.value using the units, signs and ordering explained below.
Correct any rejected inputs and rerun; interpret the result within the stated model and its assumptions.

Input field	Meaning	Value type	Requirement
actualIndependentlyQualifiedT1BlackHoleEligibilityFlags	Actual Independently Qualified T1 Black Hole Eligibility Flags. Use the units and matching context stated in the calculation below.	array of number	Required; other constraints below

Require a nonempty list of independently qualified binary eligibility flags. Sum flags and return the qualifying candidate count. A zero flag contributes no candidate; no intensity threshold, segmentation or lesion qualification is computed.

count=sum(qualifiedEligibilityFlags)

Apply the input ordering, units and branch conditions stated above.

Output field	Meaning	Value type
result	Object containing the calculated or selected fields below.	object
result.value	Sum of independently qualified binary lesion eligibility flags.	number

json

{
  "input": {
    "actualIndependentlyQualifiedT1BlackHoleEligibilityFlags": [
      1,
      0,
      1
    ]
  },
  "output": {
    "result": {
      "value": 2
    }
  }
}

Limitations and assumptions

The calculation uses the supplied observations and model inputs. It does not collect measurements, select a reference population or coefficient, or establish that the model applies to the actual case. The stated branch conditions and output units remain part of the result.

All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.

Alternative or Complementary methods

Use Peak Voxel Intensity Calculator for a complementary operation: A peak voxel intensity is the largest value within an independently qualified voxel set.

Use T2 Lesion Load Calculator for a complementary operation: T2 lesion load can be represented by the total physical volume of independently qualified disjoint lesion labels.

References

Black Hole Count Tool — tool.io

Similar or alternative tools

Peak Voxel Intensity Calculator
A peak voxel intensity is the largest value within an independently qualified voxel set.
T2 Lesion Load Calculator
T2 lesion load can be represented by the total physical volume of independently qualified disjoint lesion labels.