Berit B. Booth

Higher Diagnostic Accuracy of an AI Model for Colposcopy Compared With Conventional and Digital Colposcopic Evaluation

Objective: Colposcopy involves subjective visual assessment of cervical features that may indicate cervical dysplasia. Pattern recognition during colposcopy could be enhanced by artificial intelligence (AI). Using colposcopy images with precisely mapped multiple biopsy sites and corresponding histologic diagnoses, we developed an AI model, Cervix-AID-Net, to classify colposcopy images into low-grade disease [less than cervical intraepithelial neoplasia (CIN) grade 2] and high-grade disease (CIN grade 2 or above). The objective of this study was to compare the diagnostic performance of the Cervix-AID-Net model with the digital colposcope (DySIS) color map and colposcopists’ interpretations of the cervix in identifying low-grade and high-grade disease. Methods: The authors used 3,153 colposcopy images from 178 women, each with 4 biopsies, to train and validate the algorithm. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated with 95% CIs. Results: Cervix-AID-Net achieved a diagnostic accuracy of 99.8% (95% CI: 99.6–99.9) in classifying colposcopy images into low-grade and high-grade categories. This was significantly higher than the DySIS color map accuracy of 58.8% (95% CI: 51.1–66.1) and the accuracy of the colposcopist’s visual impression of the cervix (55.1%, 95% CI: 47.2%–62.5%). Conclusion: This first version of the Cervix-AID-Net demonstrated superior diagnostic accuracy compared with both the DySIS color map and colposcopists’ visual assessment. The results need confirmation in a prospective clinical trial.

Can the dynamic spectral imaging (DSI) color map improve colposcopy examination for precancerous cervical lesions? A prospective evaluation of the DSI color map in a multi-biopsy clinical setting

Abstract Background Colposcopy serves as a subjective examination of the cervix with low sensitivity to detect cervical intraepithelial dysplasia (CIN) grade 2 or worse (CIN2 +). Dynamic spectral imaging (DSI) colposcopy has been developed to provide an objective element to cervix examinations and has been proven to increase sensitivity of detecting CIN2 + . We aimed to assess the performance of the DSI color map and compared it to histological diagnoses of cervical biopsies in determining the CIN grade present. Methods Women were included in a consecutive, prospective manner at Randers Regional Hospital, Denmark. Women were eligible to participate if they were referred for colposcopy due to abnormal cervical smear (threshold:  ≥ ASCUS) or follow-up after previously diagnosed CIN. All women had four biopsies taken, one directed by colposcopists alone prior to viewing the DSI color map, one directed by the worst color on the respective DSI color map, and two additional biopsies. All biopsies were analyzed separately. We calculated sensitivity, specificity, positive predictive values, and negative predictive values (NPVs) with 95% confidence intervals (CIs). Results A total of 800 women were recruited. Of these, 529 (66.1%) were eligible for inclusion. The sensitivity of the DSI color map was found to be 48.1% (95% CI 41.1–55.1) in finding CIN grade 2 or worse (CIN2 +) when compared to the histological diagnosis of the DSI directed biopsy. This was 42.5% (95% CI 36.7–48.5) when compared to the final histological diagnosis of all four cervical biopsies and with an NPV of 53.5% (95% CI 50.5–56.5). Conclusion The worst color indicated by the DSI map might not consistently reflect the true grade of cervical dysplasia present. Thus, even though the DSI color map indicates low-grade changes, colposcopists should still consider taking biopsies from the area as high-grade changes might be present. Trial registration: NCT04249856, January 31 2020 (retrospectively registered).

Negative margins and negative HPV tests after large loop excision of the transformation zone: A nationwide historical cohort study

Abstract Introduction Adequate treatment of cervical precancer, defined as negative margins and a negative HPV test post‐treatment, is essential for cervical cancer prevention, as inadequate excision of cervical precancer and a positive HPV test are associated with increased risk of recurrence. Here, we aimed to describe trends in the proportion of women receiving adequate treatment in Denmark. Material and Methods Nationwide historical cohort study including Danish women ≥18 years treated with large loop excision of the transformation zone (LLETZ) from 2013 to 2022. Using the Danish Pathology Register, we collected information on all LLETZ procedures performed due to cervical intraepithelial neoplasia grade 1 or worse (CIN1+). We calculated the proportion of negative margins and negative HPV tests post‐treatment, overall and stratified by histology, age, calendar time, and residing region. Results were mainly reported descriptively. Results We included 41 128 women (median age: 35 years, interquartile range: 28–44). A total of 20 744 (50.4%) had negative margins, with the lowest proportion in women with CIN3 (47.2%) or adenocarcinoma in situ (38.3%). The proportion of negative margins declined with increasing age, from 54.1% in women aged 18–29 years to 43.6% in women aged ≥50 years. Overall, 62.4% had a negative HPV test, with no major differences observed across histology groups. The proportion of negative HPV tests ranged from 62.8% to 66.9% in women aged 18–49, whereas it was lower in women aged ≥50 years (48.4%). Conclusions Women aged ≥50 years were less likely to have negative margins and a negative HPV test post‐treatment compared to women aged 18–49. These findings prompt a need to optimize treatment in this group of women to reduce the risk of cervical cancer.

An explainable attention model for cervical precancer risk classification using colposcopic images

Cervical cancer remains a major worldwide health issue, with high morbidity and mortality rates if diagnosed and treated at a later stage. Early identification and risk assessment are crucial for preventive interventions. This paper presents the Cervix-AID-Net model for classifying cervical precancer risk using still images captured from a DYSIS colposcope. The study designs and evaluates the proposed Cervix-AID-Net model to classify high-risk and low-risk cervical precancer classes. The model comprises a Convolutional Block Attention Module (CBAM) and convolutional layers that extract interpretable and representative features from colposcopic images to distinguish high-risk and low-risk cervical precancer. In addition, the proposed Cervix-AID-Net model integrates gradient class activation maps, Local Interpretable Model-agnostic Explanations, CartoonX, and pixel rate distortion techniques to explain model decisions using output feature maps and input features. The evaluation using holdout and ten-fold cross-validation techniques yielded classification accuracies of 99.33% and 99.81%, respectively. The analysis revealed that CartoonX provides meticulous explanations for the decision of the Cervix-AID-Net model due to its ability to provide the relevant piecewise smooth part of the image. The effect of Gaussian noise and blur on the input shows that the performance remains unchanged up to Gaussian noise of 3% and blur of 10%, while the performance decreases thereafter. A comparison study of the proposed model's performance with other deep learning approaches highlights the Cervix-AID-Net model's potential as a supplemental tool for increasing the effectiveness of cervical precancer risk assessment. The proposed method, which incorporates CBAM and explainable artificial intelligence, has the potential to influence the prevention and early detection of cervical cancer. Thus, the proposed framework will help improve patient outcomes and reduce the worldwide burden of this preventable disease.

Colposcopy and Dynamic Spectral Imaging (DSI)

This study evaluates the Dynamic Spectral Imaging (DSI) Colposcope (DySIS) in it's ability to diagnose cervical dysplasia. Half of participants with be examined by the DySIS colposcope, there the other half will be examined by standard colposcopy.