AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis.

Background Few studies have evaluated the role of artificial intelligence (AI) in prior screening mammography. Purpose To examine AI risk scores assigned to screening mammography in women who were later diagnosed with breast cancer. Materials and Methods Image data and screening information of examinations performed from January 2004 to December 2019 as part of BreastScreen Norway were used in this retrospective study. Prior screening examinations from women who were later diagnosed with cancer were assigned an AI risk score by a commercially available AI system (scores of 1-7, low risk of malignancy; 8-9, intermediate risk; and 10, high risk of malignancy). Mammographic features of the cancers based on the AI score were also assessed. The association between AI score and mammographic features was tested with a bivariate test. Results A total of 2787 prior screening examinations from 1602 women (mean age, 59 years ± 5.1 [SD]) with screen-detected (n = 1016) or interval (n = 586) cancers showed an AI risk score of 10 for 389 (38.3%) and 231 (39.4%) cancers, respectively, on the mammograms in the screening round prior to diagnosis. Among the screen-detected cancers with AI scores available two screening rounds (4 years) before diagnosis, 23.0% (122 of 531) had a score of 10. Mammographic features were associated with AI score for invasive screen-detected cancers (P < .001). Density with calcifications was registered for 13.6% (43 of 317) of screen-detected cases with a score of 10 and 4.6% (15 of 322) for those with a score of 1-7. Conclusion More than one in three cases of screen-detected and interval cancers had the highest AI risk score at prior screening, suggesting that the use of AI in mammography screening may lead to earlier detection of breast cancers. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Mehta in this issue.

Perioperative use of cerebral and renal near-infrared spectroscopy in neonates: a 24-h observational study.

BACKGROUND: Neonates undergoing surgery and intensive care still carry a significant morbidity and mortality often related to hypoxic/ischemic events; some of which may go undetected by conventional monitoring. Near-infrared spectroscopy (NIRS) is a noninvasive, continuous method of measuring regional tissue oxygen saturation, and may be used to supplement conventional monitoring to improve neonatal perioperative care. However, high costs and lack of evidence regarding improved outcomes have minimized wider perinatal use of NIRS. The aim of this study was to investigate the applicability of NIRS in neonates and premature infants undergoing noncardiac surgeries. METHOD: Neonates were monitored with both cerebral and renal NIRS for 24 h after induction of anesthesia and compared with systemic blood pressure (BP), peripheral oxygen saturation (SpO2 ), and heart rate (HR). RESULTS: A total of 23 368 min of data were collected from 21 neonates. NIRS reported cerebral/renal hypoxia 2.8 (±8.3)%/19.3 (±25.4)% of the time intraoperatively and 9.6 (±17.0)%/9.9 (±18.9)% of the time postoperatively. A moderate positive correlation was found between SpO2 and NIRS (φcerebral = 0.371, φrenal = 0.542). BP showed a weaker positive correlation (φcerebral = 0.231, φrenal = 0.246), and HR no correlation (φcerebral = -0.083, φrenal = -0.029). NIRS reported hypoxia two to three times more frequently than SpO2 , and SpO2 readings were 10-15 s delayed compared to NIRS. Furthermore, NIRS appeared effective at detecting postoperative apnea. CONCLUSION: Near-infrared spectroscopy is an easily applicable technique that appears effective at detecting hypoxic events and postoperative apneas in neonates. The high incidences of regional hypoxia reported by NIRS in this study imply that there is a need for a more specific regional cerebral and renal monitoring. Despite some practical and economical limitations, NIRS may be considered a useful supplement to perinatal perioperative intensive care.

Norwegian radiologists' expectations of artificial intelligence in mammographic screening - A cross-sectional survey.

PURPOSE: To explore Norwegian breast radiologists' expectations of adding artificial intelligence (AI) in the interpretation procedure of screening mammograms. METHODS: All breast radiologists involved in interpretation of screening mammograms in BreastScreen Norway during 2021 and 2022 (n = 98) were invited to take part in this anonymous cross-sectional survey about use of AI in mammographic screening. The questionnaire included background information of the respondents, their expectations, considerations of biases, and ethical and social implications of implementing AI in screen reading. Data was collected digitally and analyzed using descriptive statistics. RESULTS: The response rate was 61% (60/98), and 67% (40/60) of the respondents were women. Sixty percent (36/60) reported ≥10 years' experience in screen reading, while 82% (49/60) reported no or limited experience with AI in health care. Eighty-two percent of the respondents were positive to explore AI in the interpretation procedure in mammographic screening. When used as decision support, 68% (41/60) expected AI to increase the radiologists' sensitivity for cancer detection. As potential challenges, 55% (33/60) reported lack of trust in the AI system and 45% (27/60) reported discrepancy between radiologists and AI systems as possible challenges. The risk of automation bias was considered high among 47% (28/60). Reduced time spent reading mammograms was rated as a potential benefit by 70% (42/60). CONCLUSION: The radiologists reported positive expectations of AI in the interpretation procedure of screening mammograms. Efforts to minimize the risk of automation bias and increase trust in the AI systems are important before and during future implementation of the tool.