Evaluating Face-Recognition Technology in Syndrome Diagnosis

NCT ID: NCT04709965

Last Updated: 2021-01-14

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 111 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2018-01-30
• Study Completion Date: 2020-11-25

Brief Summary

Birth defects are relatively common, occurring in 1 in 40 live born babies. They can be single, or multiple. They may occur as part of multiple malformation syndromes, often in association with growth disturbance or intellectual disability. Over 7000 rare syndromes have been identified. Thus, though they are rare they are collectively important. Understanding how a multiple malformation syndrome came about, defining what investigations and health surveillance is needed for affected children and identifying whether there is a treatment is very important for parents and professionals caring for affected children and also for genetic counselling of their extended families, since the majority will have a genetic basis. Diagnosis of these rare disorders is therefore important,but as many syndromes are rare this can be extremely difficult and requires specialist knowledge, many investigations and many hospital appointments. This study aims to determine whether using face-recognition software can improve diagnosis of rare syndromes when used in addition to current routine practice.

Detailed Description

Clinicians see over 2000 patients per year who have rare syndromes in Manchester NHS clinics. In the majority of these individuals the cause is unknown. Many will have a genetic cause, but knowing which genes to test and being able to access these tests is difficult. When a patient comes to the clinic, details of their medical and developmental history are collected , and they are examined in detail to look at their investigation results. New tests may also be ordered for patients. In many cases, subtle differences in physical features, especially facial features may provide an important clue to the underlying diagnosis. However, because many of the conditions seen are so rare and doctors may not have seen that particular condition before, the diagnosis may not be made immediately at the appointment. In those cases, permission will be sought to take photographs so that further opinions can be sought within the department or by sharing with national and sometimes international colleagues. This is routine practice. Where consent has been obtained for photos, these are then first reviewed in a departmental case-review meeting. They may then also be presented at regional, national or even international meetings aimed at syndrome diagnosis with patient consent.

The study aims to recruit patients who are attending clinics for syndrome diagnosis, and who have differences in their facial features. Such patients will undergo a full routine diagnostic work-up as outlined above. Following that, if patients have consented to having photographs taken as part of standard care, they will be asked if they would consent to upload of the facial photographs to a digital face recognition system, along with upload of a list of key clinical features, to see which diagnoses are suggested by this software.

A group of Inherited Metabolic Disease patients with known diagnoses will be included under the Faces sub-study to establish whether the technology may help to define their phenotype. This group of patients would be asked to send photos to the research team either by email via a secure email address or by post. The study would request one facial photo of each of the biological parents (where applicable and available).

There will first of all be routine discussion of patients and photographs in a case review meeting as per standard practice. Differential diagnoses will be formulated and recorded based on this. Following this, the facial photo will be uploaded to a face recognition system and suggested diagnoses from this recorded. Any diagnostic suggestions considered worthy of investigation will be followed up in line with standard practice.

The investigators will then determine whether this was made a) in the standard way b) only suggested by the face recognition software or c) utilising the two methods together.

Patients will be followed up to see if a final diagnosis is confirmed. The patient/parents will also be sent a questionnaire by email or post 6 months after recruitment. The questionnaire will ask if they had any concerns about using FDNA, if they found it helpful and if so in what way. Another questionnaire to professionals will collect information on how easy they found it to use, how helpful they found it ranging 1-5, did it alter patient management, if so how, and other comments

. The investigators will document whether utilising the software helped to arrive at an earlier diagnosis, whether it impeded the diagnosis by providing misleading or incorrect guidance, whether parents and professionals found its use acceptable and whether it impacted on the patient's management.

Conditions

Multiple Anomalies; Dysmorphic Features; Inborn Errors of Metabolism

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Main Study

Patients attending routine genetic clinic/paediatric clinic appointments for diagnosis of a multiple anomaly syndrome where distinctive facial features form part of their presenting pattern.

Group Type: OTHER

Face2Gene

Intervention Type: DIAGNOSTIC_TEST

This study investigates whether a new diagnostic intervention (Face2Gene facial recognition software) is better than using standard approach to diagnosis.

Faces Sub Study

Patients eligible to be recruited to the Faces Sub Study will have biochemically or genetically confirmed diagnosis of inborn disorder of metabolism where no well described dysmorphic facial features are known to be associated with disorder.

Group Type: OTHER

Face2Gene

Intervention Type: DIAGNOSTIC_TEST

This study investigates whether a new diagnostic intervention (Face2Gene facial recognition software) is better than using standard approach to diagnosis.

Interventions

Face2Gene

This study investigates whether a new diagnostic intervention (Face2Gene facial recognition software) is better than using standard approach to diagnosis.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Patients attending routine genetic clinic/paediatric clinic appointments for diagnosis of a multiple anomaly syndrome where distinctive facial features form part of their presenting pattern.

OR:

• Biochemically or genetically confirmed diagnosis of inborn disorder of metabolism where no well described dysmorphic facial features are known to be associated with disorder

Exclusion Criteria

• Patients under 8 months of age where face-recognition technology has not been shown to be effective.
• Patients who decline clinical photography as part of standard care.
• Patients who do not wish to consent to participation in the study even though they consent to photos being taken for standard care.

• Minimum Eligible Age: 8 Months
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

FDNA Inc.

UNKNOWN

Sponsor Role: collaborator

Manchester University NHS Foundation Trust

OTHER_GOV

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Sofia Douzgou, MD PhD FRCP

Role: PRINCIPAL_INVESTIGATOR

Manchester University NHS Foundation Trust

Locations

Manchester University NHS Foundation Trust

Manchester, Greater Manchester, United Kingdom

Countries

United Kingdom

Other Identifiers

R04765

Identifier Type: -

Identifier Source: org_study_id