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Introduction: Identification of severe blood loss and hemorrhagic shock in polytrauma patients poses a key challenge for trauma teams across the world, as there are just a few objective parameters, on which clinicians can rely. We investigated the relationship between exhaled air methane (CH 4 ) concentration and blood loss in a polytrauma patient. Decreased blood flow in the superior mesenteric artery (SMA) is one of the first compensatory responses to blood loss. Gases produced by the anaerobic flora of the intestinal segment supplied by the SMA are the primary source of exhaled CH 4 , which diffuses through the intestinal microvessels into the circulation and is finally eliminated through the lungs. We hypothesized that diminution of exhaled CH 4 indicates blood loss and tested our theory in a severely injured patient., Methods: Exhaled CH 4 concentrations of a severely injured patient were measured using a photoacoustic spectroscope (PAS) attached to the exhalation side of the breathing circuit. The primary objective was to investigate the relationship between exhaled CH 4 and conventional indicators of hemorrhage including hemoglobin (Hb) levels, base deficit (BD) values and vital parameters (heart rate and systolic blood pressure) in the early phase of in-hospital care (first 4 h)., Results: A severely injured patient was admitted with unstable hemodynamic parameters and incomplete left lower limb amputation, (Injury Severity Score: 38, 74/36 mmHg, 76 bpm). At the time of arrival, considerably lower CH 4 levels were detected (22,800 PAU) in the exhaled air. During the first 4 h fluid and massive blood resuscitation, the exhaled CH 4 levels were continuously rising in parallel with Htc and Hb values. Corresponding to these changes, BD values displayed a decreasing tendency., Discussion: Our study was conducted to characterize the changes in exhaled air CH 4 concentration in response to hemorrhagic shock and to provide data on a viable clinical use of an experimental technique. According to our results, the real-time detection of exhaled air CH 4 concentration is an applicable and promising technique for the early detection of bleeding and hemorrhagic shock in severely injured patients. Further research on large sample size and refinement of the PAS technique is required., Competing Interests: Declaration of competing interest None., (Copyright © 2024. Published by Elsevier Ltd.)

Purpose To explore the stand-alone breast cancer detection performance, at different risk score thresholds, of a commercially available artificial intelligence (AI) system. Materials and Methods This retrospective study included information from 661 695 digital mammographic examinations performed among 242 629 female individuals screened as a part of BreastScreen Norway, 2004-2018. The study sample included 3807 screen-detected cancers and 1110 interval breast cancers. A continuous examination-level risk score by the AI system was used to measure performance as the area under the receiver operating characteristic curve (AUC) with 95% CIs and cancer detection at different AI risk score thresholds. Results The AUC of the AI system was 0.93 (95% CI: 0.92, 0.93) for screen-detected cancers and interval breast cancers combined and 0.97 (95% CI: 0.97, 0.97) for screen-detected cancers. In a setting where 10% of the examinations with the highest AI risk scores were defined as positive and 90% with the lowest scores as negative, 92.0% (3502 of 3807) of the screen-detected cancers and 44.6% (495 of 1110) of the interval breast cancers were identified with AI. In this scenario, 68.5% (10 987 of 16 040) of false-positive screening results (negative recall assessment) were considered negative by AI. When 50% was used as the cutoff, 99.3% (3781 of 3807) of the screen-detected cancers and 85.2% (946 of 1110) of the interval breast cancers were identified as positive by AI, whereas 17.0% (2725 of 16 040) of the false-positive results were considered negative. Conclusion The AI system showed high performance in detecting breast cancers within 2 years of screening mammography and a potential for use to triage low-risk mammograms to reduce radiologist workload. Keywords: Mammography, Breast, Screening, Convolutional Neural Network (CNN), Deep Learning Algorithms Supplemental material is available for this article . © RSNA, 2024 See also commentary by Bahl and Do in this issue.

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.