Use of AI to identify patients to be assessed in a breast clinic on two-week wait: a retrospective cohort study

Background: The number of urgent referrals from primary care to specialist one stop breast clinics continues to rise beyond the capacity of the 2-week wait service. This study aims to use artificial intelligence (AI) to identify patients with new breast symptoms requiring a biopsy to identify those who should be prioritised for urgent breast clinic assessment. Methods: Data were collected retrospectively for patients attending one stop triple assessment breast clinic at Broomfield hospital between 1 June and 1 October 2021. PHP machine learning software was used to run AI on the data to identify patients who had a core biopsy in clinic. Results: A total of 794 cases were referred to one stop breast clinic for new breast symptoms—37 male (4.6%) and 757 female (95.3%). The average age of the patients included was 43.2 years. Five hundred thirty-six patients (67.5%) presented with a breast lump, 180 (22.7%) with breast pain, 61 (7.7%) with changes to shape or skin and 13 (1.6%) with a lump identified by their general practitioner. The patients who had a biopsy were of increased age [52.8 (SD 17.9) vs. 44.1 (SD 16.8), P <0.001], and had previous mammogram [ n =21, (31.8%) vs. n =148 (20.3%), P 0.03], previous benign breast disease [ n =9 (13.6%) vs. n =23 (3.1%), P <0.001], and increased use of HRT [ n =13 (19.7%) vs. n =53 (6.4%), P <0.001]. The sensitivity and specificity of AI with neural network algorithms were 84% and 90%, respectively. Conclusion: AI was very effective at predicting the presenting symptoms that are likely to result in biopsy and can therefore be used to identify patients who need to be seen urgently in breast clinic.