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APULSI

info
Some items of this scoring system can be extracted automatically from the image. However, please read carefully the documentation because this system contains non-visual items such as itchiness or sleeplesness. Since this is the case, you will need to provide the questionnaireResponse of those items inside your request.

What is APULSI?​

The Automatic Pressure ULcer Severity Index (APULSI) is a state-of-the-art scoring system for the severity assessment of pressure ulcers.

How to take pictures​

Ensuring the capture of high-quality photos is paramount to achieving optimal algorithm performance. A comprehensive guide featuring essential tips for image acquisition is available to assist in this endeavor. This section is exclusively dedicated to detailing the photo capture process, with the singular objective of attaining the finest outcomes in ASALT computation.

How does APULSI work?​

APULSI utilizes cutting-edge convolutional neural networks that have been trained on a vast dataset comprising thousands of images meticulously annotated by numerous experts. The algorithm quantifies the extent and intensity of erythema and combined them with symptoms. We are currently working a new version that assesses multiple visual signs.

Request APULSI score​

Relevant keys in the body of the request
{
  "requestId": "90925097-820b-403d-a75d-4cd989903df1",
  "data": {
    "type": "image",
    "modality": "clinical",
    "operator": "Practitioner",
    "bodySite": "LOWER_LEG_LEFT",
    "knownConditionForThisImage": {
      "conclusion": "Pressure ulcer"
    },
    "subject": {
      "identifier": "6ec724a0-6fa3-11eb-a15f-0242ac160004",
      "gender": "F",
      "height": 158,
      "weight": 49,
      "birthdate": "1994-04-10",
      "generalPractitioner": {
        "identifier": "44f89a8c-6f8a-11eb-9c8a-0242ac160004"
      },
      "managingOrganization": {
        "identifier": "b13cd636-327b-11ec-86b0-0242ac180004",
        "display": "Hospital Central"
      }
    },
    "scoringSystems": ["APULSI", "DLQI"],
    "questionnaireResponse": {
    "APULSI": {
        "odor_apusa": 1,
        "pain_apusa": 0,
        "erythema_surface": 2
      },
      "DLQI": {
        "question1": 2,
        "question2": 3,
        "question3": 1,
        "question4": 2,
        "question5": 1,
        "question6": 2,
        "question7": 2,
        "question8": 2,
        "question9": 3,
        "question10": 2
      }
    },
    "content": "base64 image"
  }
}
Check out the documentation
It is important that you check out the section Output of this documentation to understand how you should build the request and what will the response look like. Keep in mind that this is a snippet of a larger JSON file.

Questionnaire for scoring system​

Relevant keys in the body of the request
{
  "code": "APULSI",
  "questions": [
    {
      "code": "odor_apusa",
      "label": "Odor",
      "description": "null",
      "answers": [
        { "label": "No", "value": "0" },
        { "label": "Yes", "value": "1" },
      ],
      "openField": false
    },
    {
      "code": "pain_apusa",
      "label": "Pain",
      "description": "null",
      "answers": [
        { "label": "No", "value": "0" },
        { "label": "Yes", "value": "1" },
      ],
      "openField": false
    },
    {
      "code": "erythema_surface",
      "label": "Affected area",
      "description": "",
      "openField": true
    }
  ]
}

The measurement of erythema surface is automated. However, to convert the output into a specific metric unit, a reference marker is required. In the absence of this marker, the alternative is to input the value manually.

Response​

Relevant keys in the body of the response
"evolution": {
  "domains": {
    "APULSI": {
      "explainabilityMedia": {
        "content": "base 64 image",
        "detections": null
      },
      "facets": {
        "erythemaIntensity": {
          "intensity": 40,
          "value": 4
        }
      },
      "grade": {
        "category": null,
        "score": 3
      }
    }
  }
}

The APULSI does not have a translation into the more clinician-friendly concepts of “mild,” “moderate,” “severe,” or “very severe”, and therefore a null output is given for the category key.

Check out the documentation
It is important that you check out the section Output of this documentation to understand how you should build the request and what will the response look like. Keep in mind that this is a snippet of a larger JSON file.