Accuracy of an artificial intelligence as a medical device as part of a UK-based skin cancer teledermatology service.

Introduction: An artificial intelligence as a medical device (AIaMD), built on convolutional neural networks, has demonstrated high sensitivity for melanoma. To be of clinical value, it needs to safely reduce referral rates. The primary objective of this study was to demonstrate that the AIaMD had a higher rate of correctly classifying lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatology standard of care (SoC), while achieving the same sensitivity to detect malignancy. Secondary endpoints included the sensitivity, specificity, positive and negative predictive values, and number needed to biopsy to identify one case of melanoma or squamous cell carcinoma (SCC) by both the AIaMD and SoC. Methods: This prospective, single-centre, single-arm, masked, non-inferiority, adaptive, group sequential design trial recruited patients referred to a teledermatology cancer pathway (clinicaltrials.gov NCT04123678). Additional dermoscopic images of each suspicious lesion were taken using a smartphone with a dermoscopic lens attachment. The images were assessed independently by a consultant dermatologist and the AIaMD. The outputs were compared with the final histological or clinical diagnosis. Results: A total of 700 patients with 867 lesions were recruited, of which 622 participants with 789 lesions were included in the per-protocol (PP) population. In total, 63.3% of PP participants were female; 89.0% identified as white, and the median age was 51 (range 18-95); and all Fitzpatrick skin types were represented including 25/622 (4.0%) type IV-VI skin. A total of 67 malignant lesions were identified, including 8 diagnosed as melanoma. The AIaMD sensitivity was set at 91 and 92.5%, to match the literature-defined clinician sensitivity (91.46%) as closely as possible. In both settings, the AIaMD identified had a significantly higher rate of identifying lesions that did not need a biopsy or urgent referral compared to SoC (p-value = 0.001) with comparable sensitivity for skin cancer. Discussion: The AIaMD identified significantly more lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatologists. This has the potential to reduce the burden of unnecessary referrals when used as part of a teledermatology service.

Severe progressive scarring pembrolizumab-induced lichen planopilaris in a patient with metastatic melanoma.

Lichenoid reactions are one of the many cutaneous immune-related adverse events seen with the use of immune checkpoint inhibitors, particularly anti-PD1 inhibitors. We present a rare care of severe lichen planopilaris secondary to pembrolizumab, with progression even after cessation of immunotherapy. It is important to recognise the significant long-term impact of these cutaneous adverse effects on patient's quality of life.

Unusual presentation of a spinal epidural abscess.

A 59-year-old female patient presented with a 3-day history of abdominal pain and confusion. She had no significant medical history. On admission her Glasgow Coma Scale was 14/15; she was septic and examination revealed right upper quadrant tenderness. She deteriorated quickly, becoming acidotic and hypoxic, and was sedated, intubated and transferred to the intensive care unit. Blood tests revealed raised inflammatory markers and blood cultures grew Staphylococcus aureus. Initial CT head revealed raised intracranial pressure and she was treated for meningoencephalitis. Repeat CT head 12 days later showed resolving oedema, and a lumbar puncture was attempted. This drew only frank pus and an abscess was suspected. CT spine confirmed a possible paravertebral abscess. Once extubated, MRI spine was possible which confirmed spinal epidural abscesses-1 month postadmission. These were rapidly drained by the neurosurgical team and the patient is currently receiving rehabilitation in a specialist centre.