Endpoint Specification

This section of the documentation provides an overview of the data schemas for the medical device's API endpoints.

If you encounter issues accessing or interpreting the content, refer to the troubleshooting section or contact our support team for assistance.

Explanation

The device operates in a straightforward manner: you send images, and you receive a DiagnosticReport as defined in HL7's FHIR® specifications. The following chart illustrates the basic workflow:

Diagnostic Support

While the term diagnosis is commonly used to describe the device's output, it is important to note that the device provides a interpretative distribution representation of possible International Classification of Diseases (ICD) categories that might be represented in the pixels content of the image. Healthcare practitioners and organizations may use this output to inform a diagnosis, but the device itself does not generate a diagnosis. This distinction is clearly indicated in the output, which adheres to the FHIR standard and is labeled as a DiagnosticReport with a status of preliminary.

Severity Measure

Similarly, while the term severity measure is often used, the device outputs a quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others. This output helps healthcare practitioners assess the degree of a patient's condition, but it is not the severity itself. This is also indicated in the FHIR-compliant DiagnosticReport with a preliminary status.

Clinical Indicators

Clinical indicators are derived from the diagnostic support output of the device, which is a interpretative distribution representation of possible International Classification of Diseases (ICD) categories that might be represented in the pixels content of the image. This output is a probability distribution where each ICD-11 category is assigned a probability value between 0 and 1, with the total sum equaling 1.

Condition Confirmation

The device's output covers a wide range of ICD-11 categories, except for one: Non-specific lesion. This category is used when no condition is detected in the image. The likelihood of an image containing any condition (hasCondition) can be calculated as:

$$p_{condition}= 1 - p_{nsl}$$

Where $p_{nsl}$ is the probability assigned to the Non-specific lesion category. Since the total probability equals 1, subtracting $p_{nsl}$ gives the probability of a condition being present.

Weighted Sum Findings

Several clinical indicators (pigmentedLesion, urgentReferral, highPriorityReferral, and malignancy) are calculated using a weighted sum of the device's output. Each indicator uses binary weights (0 or 1) to determine which ICD-11 categories contribute to its value.

• Pigmented Lesion (pigmentedLesion): Positive weights for categories corresponding to pigmented lesions.
• Urgent Referral (urgentReferral): Positive weights for categories requiring referral within 0-48 hours.
• High Priority Referral (highPriorityReferral): Positive weights for categories requiring referral within 7-15 days.
• Malignancy (malignancy): Positive weights for categories related to malignancy (e.g., skin cancer).

The value of each finding ($f$) is computed as:

$$f = \sum_{i=1}^{N} w_i \cdot p_i$$

Where $N$ is the total number of predicted ICD-11 categories, and $w_i$ and $p_i$ are the weight and probability of the $i$-th category.

ICD-11 Categories Related to Malignancy

ICD-11 code	Category
2C30.3	Acral lentiginous melanoma (primary)
2C33	Adnexal carcinoma
2E63.00	Amelanotic malignant melanoma
2B56.1	Angiosarcoma
2C32	Basal cell carcinoma
2D41	Carcinoma
2C30	Cutaneous melanoma
2B0Z	Cutaneous T-cell lymphoma
2B53.Y	Dermatofibrosarcoma protuberans
2B57.Z	Kaposi sarcoma
2C34	Cutaneous neuroendocrine carcinoma (Merkel cell carcinoma)
2E08	Metastatic malignant neoplasm involving skin
1G60.0	Mycetoma
2B01	Mycosis fungoides
2B0Y	Pleomorphic T cell lymphoma
2C31	Squamous cell carcinoma
EB13	Stevens Johnson syndrome or toxic epidermal necrolysis
2C32.2	Superficial basal cell carcinoma
1A6Z	Syphilis

#####Binary Indicators

Binary indicators are derived from the ICD-11 probability distribution as a post-processing step using a dermatologist-defined mapping matrix. The protocol for creating, validating, and maintaining this matrix is defined in R-TF-028-004 Data Annotation Instructions - Binary Indicator Mapping. Each indicator reflects the aggregated probability that a case belongs to clinically meaningful categories requiring differential triage or diagnostic attention.

The six binary indicators are:

1. Malignant: probability that the lesion is classified as a confirmed malignancy (e.g., melanoma, squamous cell carcinoma, basal cell carcinoma).
2. Pre-malignant: probability of categories with malignant potential (e.g., actinic keratosis, Bowen's disease).
3. Associated with malignancy: benign or inflammatory categories with frequent overlap or mimicry of malignant presentations (e.g., atypical nevi, pigmented seborrheic keratoses).
4. Pigmented lesion: probability that the lesion belongs to the pigmented subgroup, important for melanoma probability assessment.
5. Urgent referral: lesions associated with categories typically requiring dermatological evaluation within 48 hours (e.g., suspected melanoma, rapidly growing nodular lesions, bleeding or ulcerated malignancies).
6. High-priority referral: lesions that should be seen within 2 weeks according to dermatology referral guidelines (e.g., suspected non-melanoma skin cancer, premalignant lesions with malignant potential).

For $N$ categories and 6 indicators, the mapping matrix has a size of $N \times 6$. Thus, the computation of each indicator $j \in [1, 2, \ldots, 6]$ is defined as:

$$\text{Binary Indicator}_j = \sum_{i=1}^{n} \big(M_{ij} \times p_i\big)$$

where $p_i$ is the probability for the $i$-th ICD-11 category, and $M_{ij}$ is the binary weight coefficient ($M_{ij} \in [0, 1]$) that indicates whether category $i$ contributes to indicator $j$.

Performance Indicators

Performance indicators provide insights into the device's skin disease recognition performance. These indicators are derived from the diagnostic support output, which is a interpretative distribution representation of possible International Classification of Diseases (ICD) categories that might be represented in the pixels content of the image.

Entropy

Entropy estimates the uncertainty of the device's output. Normalized entropy ($H$) is calculated as:

$$H=-\frac{\sum_{i=1}^{N}p_i \cdot \ln(p_i)}{\ln(N)}$$

Where $\ln$ is the natural logarithm, $N$ is the total number of ICD-11 categories, and $p_i$ is the probability of the $i$-th category.

Low entropy indicates high confidence, while high entropy suggests low confidence.