scholarly journals Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov

2020 ◽  
Vol 10 ◽  
Author(s):  
Jingsi Dong ◽  
Yingcai Geng ◽  
Dan Lu ◽  
Bingjie Li ◽  
Long Tian ◽  
...  
BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.


PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0198437 ◽  
Author(s):  
Lisa Jane Mackenzie ◽  
Mariko Leanne Carey ◽  
Eiji Suzuki ◽  
Robert William Sanson-Fisher ◽  
Hiromi Asada ◽  
...  

2020 ◽  
Vol 16 (5) ◽  
Author(s):  
Allan B. Smith ◽  
Anita Y. Niu ◽  
Joseph Descallar ◽  
Geoff P. Delaney ◽  
Verena S. Wu ◽  
...  

BMJ Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. e033008 ◽  
Author(s):  
Clare Pearson ◽  
Veronique Poirier ◽  
Karen Fitzgerald ◽  
Greg Rubin ◽  
Willie Hamilton

IntroductionPatients presenting to primary care with site-specific alarm symptoms can be referred onto urgent suspected cancer pathways, whereas those with non-specific symptoms currently have no dedicated referral routes leading to delays in cancer diagnosis and poorer outcomes. Pilot Multidisciplinary Diagnostic Centres (MDCs) provide a referral route for such patients in England.ObjectivesThis work aimed to use linked primary care and cancer registration data to describe diagnostic pathways for patients similar to those being referred into MDCs and compare them to patients presenting with more specific symptoms.MethodsThis cross-sectional study linked primary care data from the National Cancer Diagnosis Audit (NCDA) to national cancer registration and Route to Diagnosis records. Patient symptoms recorded in the NCDA were used to allocate patients to one of two groups - those presenting with symptoms mirroring referral criteria of MDCs (non-specific but concerning symptoms (NSCS)) and those with at least one site-specific alarm symptom (non-NSCS). Descriptive analyses compared the two groups and regression analysis by group investigated associations with long primary care intervals (PCIs).ResultsPatients with NSCS were more likely to be diagnosed at later stage (32% stage 4, compared with 21% in non-NSCS) and via an emergency presentation (34% vs 16%). These patients also had more multiple pre-referral general practitioner consultations (59% vs 43%) and primary care-led diagnostics (blood tests: 57% vs 35%). Patients with NSCS had higher odds of having longer PCIs (adjusted OR: 1.24 (1.11 to 1.36)). Patients with lung and urological cancers also had higher odds of longer PCIs overall and in both groups.ConclusionsDifferences in the diagnostic pathway show that patients with symptoms mirroring the MDC referral criteria could benefit from a new referral pathway.


2018 ◽  
Vol 5 (2) ◽  
pp. 205510291881531 ◽  
Author(s):  
Chiara Marzorati ◽  
Luca Bailo ◽  
Ketti Mazzocco ◽  
Gabriella Pravettoni

The caregivers’ perceptions of the patients’ health condition may be biased and induce them to perceive higher needs than patients actually disclose. Our aim was to assess if the level of knowledge and awareness about cancer disease and treatment, and patient participation and assistance differs between caregivers and patients. A descriptive, cross-sectional study was conducted across five countries (Italy, United Kingdom, Spain, France and Germany) on a total of 510 participants who directly (patient) or indirectly (caregiver) faced a cancer diagnosis. Investigating this divergence could help to identify possible difficulties in patient–caregiver relationship, eventually improving patient empowerment.


2015 ◽  
Vol 21 (5) ◽  
pp. 562-570 ◽  
Author(s):  
Graça Cardoso ◽  
Joao Graca ◽  
Catarina Klut ◽  
Bruno Trancas ◽  
Ana Papoila

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