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All Journal Narra J Indonesian Journal of Cancer Makara Journal of Health Research
Afifi, Rahmi
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Biochemistry, Genetics & Molecular Biology Health Professions Immunology & microbiology Medicine & Pharmacology Public Health

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Impact of Artificial Intelligence on Mammography Interpretation by Breast Radiologists, Non-Breast Radiologists, and Senior Residents Darmiati, Sawitri; Afifi, Rahmi; Billy, Christy Amanda; Panigoro, Sonar Soni; Kartini, Diani; Prihartono, Joedo
Indonesian Journal of Cancer Vol 17, No 4 (2023): December
Publisher : http://dharmais.co.id/

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33371/ijoc.v17i4.1100

Abstract

Background: Artificial intelligence (AI) is recognized to have tremendous potential to revolutionize breast cancer management through mammography. However, the extent of its impact on radiologists with different levels of experience remains largely unexplored. Therefore, this study aimed to comprehensively show how AI could assist radiologists of varying expertise including breast and non-breast radiologists, as well as senior residents, in performing mammogram interpretation.Methods: This retrospective study analyzed eligible mammograms from Cipto Mangunkusumo Hospital between January 2017 and March 2021. Mammographic readings were conducted independently by two breast radiologists, two from other subspecialties, and three senior residents, all blinded to clinical information. AI standalone performance, as well as radiologists with and without AI assistance, was measured. Results: The results showed that a total of 886 eligible mammograms were analyzed. AI standalone performance, assessed using ROC curve analysis, yielded an AUC of 0.946 (95% CI, 0.925–0.967) with sensitivity and specificity of 90.1% and 93.6%, respectively. AI assistance significantly improved the sensitivity and specificity of all radiologists, regardless of experience level, with a median increase of 19.4% (IQR, 10.4–33.5%) and 12.1% (IQR, 5.2–16.2%), respectively. Moreover, there was a trend toward a higher increase with AI assistance in dense compared to fatty breasts.Conclusions: AI proved to be a highly effective diagnostic supplement for radiologists across varying experience levels, specifically in non-breast radiologists, offering the potential to add even greater value in cases of dense breast tissue. The results were derived from a national referral tertiary hospital that generally received many breast cancer cases referred from other hospitals for further treatment. Therefore, further studies incorporating different levels of hospitals were needed.
Designing the CORI score for COVID-19 diagnosis in parallel with deep learning-based imaging models Kamelia, Telly; Zulkarnaien, Benny; Septiyanti, Wita; Afifi, Rahmi; Krisnadhi, Adila; Rumende, Cleopas M.; Wibisono, Ari; Guarddin, Gladhi; Chahyati, Dina; Yunus, Reyhan E.; Pratama, Dhita P.; Rahmawati, Irda N.; Nareswari, Dewi; Falerisya, Maharani; Salsabila, Raissa; Baruna, Bagus DI.; Iriani, Anggraini; Nandipinto, Finny; Wicaksono, Ceva; Sini, Ivan R.
Narra J Vol. 5 No. 2 (2025): August 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i2.1606

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has triggered a global health crisis and placed unprecedented strain on healthcare systems, particularly in resource-limited settings where access to RT-PCR testing is often restricted. Alternative diagnostic strategies are therefore critical. Chest X-rays, when integrated with artificial intelligence (AI), offers a promising approach for COVID-19 detection. The aim of this study was to develop an AI-assisted diagnostic model that combines chest X-ray images and clinical data to generate a COVID-19 Risk Index (CORI) Score and to implement a deep learning model based on ResNet architecture. Between April 2020 and July 2021, a multicenter cohort study was conducted across three hospitals in Jakarta, Indonesia, involving 367 participants categorized into three groups: 100 COVID-19 positive, 100 with non-COVID-19 pneumonia, and 100 healthy individuals. Clinical parameters (e.g., fever, cough, oxygen saturation) and laboratory findings (e.g., D-dimer and C-reactive protein levels) were collected alongside chest X-ray images. Both the CORI Score and the ResNet model were trained using this integrated dataset. During internal validation, the ResNet model achieved 91% accuracy, 94% sensitivity, and 92% specificity. In external validation, it correctly identified 82 of 100 COVID-19 cases. The combined use of imaging, clinical, and laboratory data yielded an area under the ROC curve of 0.98 and a sensitivity exceeding 95%. The CORI Score demonstrated strong diagnostic performance, with 96.6% accuracy, 98% sensitivity, 95.4% specificity, a 99.5% negative predictive value, and a 91.1% positive predictive value. Despite limitations—including retrospective data collection, inter-hospital variability, and limited external validation—the ResNet-based AI model and the CORI Score show substantial promise as diagnostic tools for COVID-19, with performance comparable to that of experienced thoracic radiologists in Indonesia.
Ratio of Vascular Pedicle Width and Thoracic Diameter to Differentiate Cardiogenic and Non-Cardiogenic Pulmonary Edema Afifi, Rahmi; Fachri, Achmad; Madjid, Amir Sjarifuddin; Prihartono, Joedo; Prasetyo, Marcel; Christian, Andreas
Makara Journal of Health Research Vol. 26, No. 3
Publisher : UI Scholars Hub

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Abstract

Background: Excess intravascular volume evaluation is essential in the intensive care unit (ICU); however, clinical information to differentiate cardiogenic and non-cardiogenic pulmonary edema has been proven ineffective. Thus, this study aimed to distinguish cardiogenic from non-cardiogenic pulmonary edema using the ratio of vascular pedicle width (VPW) to thoracic diameter (VPTR). Methods: This cross-sectional study was conducted based on secondary data from chest radiographs of 100 patients with clinical symptoms of pulmonary edema in the ICU from January 2013 to December 2015. Cardiogenic and non-cardiogenic pulmonary edema were distinguished using VPW and cardiothoracic ratio measurements (CTR). VPTR was measured to differentiate between the two types of pulmonary edema, and the cut-off value was obtained using a receiver operating characteristic curve. Results: This study revealed a prevalence of 21% and 79% for cardiogenic and non-cardiogenic pulmonary edema, respectively. A VPTR cut-off value of 25.1% with a sensitivity of 90% and specificity of 86%, may distinguish cardiogenic from non-cardiogenic pulmonary edema. Conclusions: VPTR is an alternative method to differentiate between cardiogenic and non-cardiogenic pulmonary edema, and this ratio measurement is useful in cases where radiograph films are not standardized.
Co-Authors Andreas Christian Anggraini Iriani Ari Wibisono Baruna, Bagus DI. Billy, Christy Amanda Chahyati, Dina Darmiati, Sawitri Diani Kartini Fachri, Achmad Falerisya, Maharani Guarddin, Gladhi Joedo Prihartono Krisnadhi, Adila Madjid, Amir Sjarifuddin Marcel Prasetyo Nandipinto, Finny Nareswari, Dewi Pratama, Dhita P. Rahmawati, Irda N. Rumende, Cleopas M. Salsabila, Raissa Septiyanti, Wita Sini, Ivan R. Sonar Soni Panigoro Telly Kamelia Wicaksono, Ceva Yunus, Reyhan E. Zulkarnaien, Benny