Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation.

Weight Rotation as a Regularization Strategy in Convolutional Neural Networks.

Convolutional Neural Networks (CNN) have become the gold standard in many visual recognition tasks including medical applications. Due to their high variance, however, these models are prone to over-fit the data they are trained on. To mitigate this problem, one of the most common strategies, is to perform data augmentation. Rotation, scaling and translation are common operations. In this work we propose an alternative method to rotation-based data augmentation where the rotation transformation is performed inside the CNN architecture. In each training batch the weights of all convolutional layers are rotated by the same random angle. We validate our proposed method empirically showing its usefulness under different scenarios.

[The occurrence and distribution of lymphatic filariasis in Greater Metropolitan Recife: the case of an endemic area in Jaboatão dos Guararapes, Pernambuco, Brazil]. / Situação da filariose bancroftiana na Região Metropolitana do Recife: estudo em uma área endêmica no Município de Jaboatão dos Guararapes, Pernambuco, Brasil.

This article analyzes the results of an epidemiological survey on the occurrence and distribution of lymphatic filariasis in Cavaleiro, a district in the city of Jaboatão dos Guararapes in Greater Metropolitan Recife, Pernambuco State, Brazil. The district was divided into 12 areas, and a cross-sectional technique was used to examine 9,520 individuals, of whom 2.2% were found to be infected. There were positive cases in 11 of the areas. The prevalence rate ranged from 0% to 5.15%. The majority (64.3%) of infected individuals were males (p < 0.05). Infected individuals were identified in all age groups. The results show that lymphatic filariasis is still an important public health problem in the Cavaleiro district and that it deserves attention by municipal authorities and health planners.

Situaçäo da filariose bancroftiana na Regiäo Metropolitana do Recife: estudo em uma área endêmica no Município de Jaboatäo dos Guararapes, Pernambuco, Brasil / The occurrence and distribution of lymphatic filariasis in Greater Metropolitan Recife: the case of an endemic area in Jaboatäo dos Guararapes, Pernambuco, Brazil

Este artigo apresenta os resultados do inquérito epidemiológico que estudou a ocorrência e a distribuiçäo da filariose bancroftiana no Distrito de Cavaleiro, Município de Jaboatäo dos Guararapes, Pernambuco. O desenho de estudo empregado foi o seccional. Foram analisados 9.520 indivíduos da populaçäo residente nos 12 bairros que compöem o distrito. Deste total, detectou-se 213 microfilarêmicos (2,2 por cento). Cerca de 91,7 por cento dos bairros pesquisados apresentaram casos de infecçäo filarial, com prevalências variando de 0 por cento a 5,15 por cento. A populaçäo masculina foi a mais acometida pela infecçäo, com 64,3 por cento dos casos (p < 0,05). Verificou-se, também, a existência de positividade em todas as faixas etárias examinadas. Esses resultados indicam que a filariose bancroftiana permanece como um problema de saúde pública na regiäo, necessitando de medidas de controle e atençäo dos administradores e planejadores de saúde

Situaçäo da filariose bancroftiana na Regiäo Metropolitana do Recife: estudo em uma área endêmica no Município de Jaboatäo dos Guararapes, Pernambuco, Brasil / The occurrence and distribution of lymphatic filariasis in Greater Metropolitan Recife: the case of an endemic area in Jaboatäo dos Guararapes, Pernambuco, Brazil

Este artigo apresenta os resultados do inquérito epidemiológico que estudou a ocorrência e a distribuiçäo da filariose bancroftiana no Distrito de Cavaleiro, Município de Jaboatäo dos Guararapes, Pernambuco. O desenho de estudo empregado foi o seccional. Foram analisados 9.520 indivíduos da populaçäo residente nos 12 bairros que compöem o distrito. Deste total, detectou-se 213 microfilarêmicos (2,2 por cento). Cerca de 91,7 por cento dos bairros pesquisados apresentaram casos de infecçäo filarial, com prevalências variando de 0 por cento a 5,15 por cento. A populaçäo masculina foi a mais acometida pela infecçäo, com 64,3 por cento dos casos (p < 0,05). Verificou-se, também, a existência de positividade em todas as faixas etárias examinadas. Esses resultados indicam que a filariose bancroftiana permanece como um problema de saúde pública na regiäo, necessitando de medidas de controle e atençäo dos administradores e planejadores de saúde (AU)