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Rai HM, Yoo J, Razaque A. Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques. EXPERT SYSTEMS WITH APPLICATIONS 2024; 255:124838. [DOI: 10.1016/j.eswa.2024.124838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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Rai HM, Yoo J, Atif Moqurrab S, Dashkevych S. Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets. MEASUREMENT 2024; 225:114059. [DOI: 10.1016/j.measurement.2023.114059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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Rai HM, Yoo J. A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics. J Cancer Res Clin Oncol 2023; 149:14365-14408. [PMID: 37540254 DOI: 10.1007/s00432-023-05216-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 07/26/2023] [Indexed: 08/05/2023]
Abstract
PURPOSE There are millions of people who lose their life due to several types of fatal diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol consumption, infections, ultraviolet radiation, smoking, and unhealthy lifestyles. Cancer is abnormal and uncontrolled tissue growth inside the body which may be spread to other body parts other than where it has originated. Hence it is very much required to diagnose the cancer at an early stage to provide correct and timely treatment. Also, manual diagnosis and diagnostic error may cause of the death of many patients hence much research are going on for the automatic and accurate detection of cancer at early stage. METHODS In this paper, we have done the comparative analysis of the diagnosis and recent advancement for the detection of various cancer types using traditional machine learning (ML) and deep learning (DL) models. In this study, we have included four types of cancers, brain, lung, skin, and breast and their detection using ML and DL techniques. In extensive review we have included a total of 130 pieces of literature among which 56 are of ML-based and 74 are from DL-based cancer detection techniques. Only the peer reviewed research papers published in the recent 5-year span (2018-2023) have been included for the analysis based on the parameters, year of publication, feature utilized, best model, dataset/images utilized, and best accuracy. We have reviewed ML and DL-based techniques for cancer detection separately and included accuracy as the performance evaluation metrics to maintain the homogeneity while verifying the classifier efficiency. RESULTS Among all the reviewed literatures, DL techniques achieved the highest accuracy of 100%, while ML techniques achieved 99.89%. The lowest accuracy achieved using DL and ML approaches were 70% and 75.48%, respectively. The difference in accuracy between the highest and lowest performing models is about 28.8% for skin cancer detection. In addition, the key findings, and challenges for each type of cancer detection using ML and DL techniques have been presented. The comparative analysis between the best performing and worst performing models, along with overall key findings and challenges, has been provided for future research purposes. Although the analysis is based on accuracy as the performance metric and various parameters, the results demonstrate a significant scope for improvement in classification efficiency. CONCLUSION The paper concludes that both ML and DL techniques hold promise in the early detection of various cancer types. However, the study identifies specific challenges that need to be addressed for the widespread implementation of these techniques in clinical settings. The presented results offer valuable guidance for future research in cancer detection, emphasizing the need for continued advancements in ML and DL-based approaches to improve diagnostic accuracy and ultimately save more lives.
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Affiliation(s)
- Hari Mohan Rai
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea.
| | - Joon Yoo
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea
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Harada Y, Watari T, Nagano H, Suzuki T, Kunitomo K, Miyagami T, Aita T, Ishizuka K, Maebashi M, Harada T, Sakamoto T, Tomiyama S, Shimizu T. Diagnostic errors in uncommon conditions: a systematic review of case reports of diagnostic errors. Diagnosis (Berl) 2023; 10:329-336. [PMID: 37561056 DOI: 10.1515/dx-2023-0030] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 06/21/2023] [Indexed: 08/11/2023]
Abstract
OBJECTIVES To assess the usefulness of case reports as sources for research on diagnostic errors in uncommon diseases and atypical presentations. CONTENT We reviewed 563 case reports of diagnostic error. The commonality of the final diagnoses was classified based on the description in the articles, Orphanet, or epidemiological data on available references; the typicality of presentation was classified based on the description in the articles and the judgment of the physician researchers. Diagnosis Error Evaluation and Research (DEER), Reliable Diagnosis Challenges (RDC), and Generic Diagnostic Pitfalls (GDP) taxonomies were used to assess the factors contributing to diagnostic errors. SUMMARY AND OUTLOOK Excluding three cases in that commonality could not be classified, 560 cases were classified into four categories: typical presentations of common diseases (60, 10.7 %), atypical presentations of common diseases (35, 6.2 %), typical presentations of uncommon diseases (276, 49.3 %), and atypical presentations of uncommon diseases (189, 33.8 %). The most important DEER taxonomy was "Failure/delay in considering the diagnosis" among the four categories, whereas the most important RDC and GDP taxonomies varied with the categories. Case reports can be a useful data source for research on the diagnostic errors of uncommon diseases with or without atypical presentations.
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Affiliation(s)
- Yukinori Harada
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Shimotsuga-Gun, Japan
| | - Takashi Watari
- General Medicine Center, Shimane University Hospital, Izumo, Japan
| | - Hiroyuki Nagano
- Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Kotaro Kunitomo
- National Hospital Organisation Kumamoto Medical Center, Kumamoto, Japan
| | | | - Tetsuro Aita
- Department of General Internal Medicine, Fukushima Medical University, Fukushima, Japan
| | - Kosuke Ishizuka
- Department of General Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | | | - Taku Harada
- Division of General Medicine, Nerima Hikarigaoka Hospital, Nerima-Ku, Tokyo
| | - Tetsu Sakamoto
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Shimotsuga-Gun, Japan
| | - Shusaku Tomiyama
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Shimotsuga-Gun, Japan
| | - Taro Shimizu
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Shimotsuga-Gun, Japan
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Harada Y, Tomiyama S, Sakamoto T, Sugimoto S, Kawamura R, Yokose M, Hayashi A, Shimizu T. Effects of Combinational Use of Additional Differential Diagnostic Generators on the Diagnostic Accuracy of the Differential Diagnosis List Developed by an Artificial Intelligence-Driven Automated History-Taking System: Pilot Cross-Sectional Study. JMIR Form Res 2023; 7:e49034. [PMID: 37531164 PMCID: PMC10433017 DOI: 10.2196/49034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/23/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Low diagnostic accuracy is a major concern in automated medical history-taking systems with differential diagnosis (DDx) generators. Extending the concept of collective intelligence to the field of DDx generators such that the accuracy of judgment becomes higher when accepting an integrated diagnosis list from multiple people than when accepting a diagnosis list from a single person may be a possible solution. OBJECTIVE The purpose of this study is to assess whether the combined use of several DDx generators improves the diagnostic accuracy of DDx lists. METHODS We used medical history data and the top 10 DDx lists (index DDx lists) generated by an artificial intelligence (AI)-driven automated medical history-taking system from 103 patients with confirmed diagnoses. Two research physicians independently created the other top 10 DDx lists (second and third DDx lists) per case by imputing key information into the other 2 DDx generators based on the medical history generated by the automated medical history-taking system without reading the index lists generated by the automated medical history-taking system. We used the McNemar test to assess the improvement in diagnostic accuracy from the index DDx lists to the three types of combined DDx lists: (1) simply combining DDx lists from the index, second, and third lists; (2) creating a new top 10 DDx list using a 1/n weighting rule; and (3) creating new lists with only shared diagnoses among DDx lists from the index, second, and third lists. We treated the data generated by 2 research physicians from the same patient as independent cases. Therefore, the number of cases included in analyses in the case using 2 additional lists was 206 (103 cases × 2 physicians' input). RESULTS The diagnostic accuracy of the index lists was 46% (47/103). Diagnostic accuracy was improved by simply combining the other 2 DDx lists (133/206, 65%, P<.001), whereas the other 2 combined DDx lists did not improve the diagnostic accuracy of the DDx lists (106/206, 52%, P=.05 in the collective list with the 1/n weighting rule and 29/206, 14%, P<.001 in the only shared diagnoses among the 3 DDx lists). CONCLUSIONS Simply adding each of the top 10 DDx lists from additional DDx generators increased the diagnostic accuracy of the DDx list by approximately 20%, suggesting that the combinational use of DDx generators early in the diagnostic process is beneficial.
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Affiliation(s)
- Yukinori Harada
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Shimotsugagun, Japan
- Department of Internal Medicine, Nagano Chuo Hospital, Nagano, Japan
| | - Shusaku Tomiyama
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Shimotsugagun, Japan
| | - Tetsu Sakamoto
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Shimotsugagun, Japan
| | - Shu Sugimoto
- Department of Internal Medicine, Nagano Chuo Hospital, Nagano, Japan
| | - Ren Kawamura
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Shimotsugagun, Japan
| | - Masashi Yokose
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Shimotsugagun, Japan
| | - Arisa Hayashi
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Shimotsugagun, Japan
| | - Taro Shimizu
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Shimotsugagun, Japan
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Yanagita Y, Shikino K, Ishizuka K, Uchida S, Li Y, Yokokawa D, Tsukamoto T, Noda K, Uehara T, Ikusaka M. Improving decision accuracy using a clinical decision support system for medical students during history-taking: a randomized clinical trial. BMC MEDICAL EDUCATION 2023; 23:383. [PMID: 37231512 PMCID: PMC10214648 DOI: 10.1186/s12909-023-04370-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/17/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND A clinical diagnostic support system (CDSS) can support medical students and physicians in providing evidence-based care. In this study, we investigate diagnostic accuracy based on the history of present illness between groups of medical students using a CDSS, Google, and neither (control). Further, the degree of diagnostic accuracy of medical students using a CDSS is compared with that of residents using neither a CDSS nor Google. METHODS This study is a randomized educational trial. The participants comprised 64 medical students and 13 residents who rotated in the Department of General Medicine at Chiba University Hospital from May to December 2020. The medical students were randomly divided into the CDSS group (n = 22), Google group (n = 22), and control group (n = 20). Participants were asked to provide the three most likely diagnoses for 20 cases, mainly a history of a present illness (10 common and 10 emergent diseases). Each correct diagnosis was awarded 1 point (maximum 20 points). The mean scores of the three medical student groups were compared using a one-way analysis of variance. Furthermore, the mean scores of the CDSS, Google, and residents' (without CDSS or Google) groups were compared. RESULTS The mean scores of the CDSS (12.0 ± 1.3) and Google (11.9 ± 1.1) groups were significantly higher than those of the control group (9.5 ± 1.7; p = 0.02 and p = 0.03, respectively). The residents' group's mean score (14.7 ± 1.4) was higher than the mean scores of the CDSS and Google groups (p = 0.01). Regarding common disease cases, the mean scores were 7.4 ± 0.7, 7.1 ± 0.7, and 8.2 ± 0.7 for the CDSS, Google, and residents' groups, respectively. There were no significant differences in mean scores (p = 0.1). CONCLUSIONS Medical students who used the CDSS and Google were able to list differential diagnoses more accurately than those using neither. Furthermore, they could make the same level of differential diagnoses as residents in the context of common diseases. TRIAL REGISTRATION This study was retrospectively registered with the University Hospital Medical Information Network Clinical Trials Registry on 24/12/2020 (unique trial number: UMIN000042831).
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Affiliation(s)
- Yasutaka Yanagita
- Department of General Medicine, Chiba University Hospital, 1-8-1, Inohana, Chuo-Ku, Chiba-City, Chiba Pref, Japan.
| | - Kiyoshi Shikino
- Department of General Medicine, Chiba University Hospital, 1-8-1, Inohana, Chuo-Ku, Chiba-City, Chiba Pref, Japan
| | - Kosuke Ishizuka
- Department of General Medicine, Chiba University Hospital, 1-8-1, Inohana, Chuo-Ku, Chiba-City, Chiba Pref, Japan
| | - Shun Uchida
- Department of General Medicine, Chiba University Hospital, 1-8-1, Inohana, Chuo-Ku, Chiba-City, Chiba Pref, Japan
| | - Yu Li
- Department of General Medicine, Chiba University Hospital, 1-8-1, Inohana, Chuo-Ku, Chiba-City, Chiba Pref, Japan
| | - Daiki Yokokawa
- Department of General Medicine, Chiba University Hospital, 1-8-1, Inohana, Chuo-Ku, Chiba-City, Chiba Pref, Japan
| | - Tomoko Tsukamoto
- Department of General Medicine, Chiba University Hospital, 1-8-1, Inohana, Chuo-Ku, Chiba-City, Chiba Pref, Japan
| | - Kazutaka Noda
- Department of General Medicine, Chiba University Hospital, 1-8-1, Inohana, Chuo-Ku, Chiba-City, Chiba Pref, Japan
| | - Takanori Uehara
- Department of General Medicine, Chiba University Hospital, 1-8-1, Inohana, Chuo-Ku, Chiba-City, Chiba Pref, Japan
| | - Masatomi Ikusaka
- Department of General Medicine, Chiba University Hospital, 1-8-1, Inohana, Chuo-Ku, Chiba-City, Chiba Pref, Japan
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Kunitomo K, Harada T, Watari T. Cognitive biases encountered by physicians in the emergency room. BMC Emerg Med 2022; 22:148. [PMID: 36028810 PMCID: PMC9414136 DOI: 10.1186/s12873-022-00708-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 08/12/2022] [Indexed: 11/26/2022] Open
Abstract
Background Diagnostic errors constitute an important medical safety problem that needs improvement, and their frequency and severity are high in emergency room settings. Previous studies have suggested that diagnostic errors occur in 0.6-12% of first-time patients in the emergency room and that one or more cognitive factors are involved in 96% of these cases. This study aimed to identify the types of cognitive biases experienced by physicians in emergency rooms in Japan. Methods We conducted a questionnaire survey using Nikkei Medical Online (Internet) from January 21 to January 31, 2019. Of the 159,519 physicians registered with Nikkei Medical Online when the survey was administered, those who volunteered their most memorable diagnostic error cases in the emergency room participated in the study. EZR was used for the statistical analyses. Results A total of 387 physicians were included. The most common cognitive biases were overconfidence (22.5%), confirmation (21.2%), availability (12.4%), and anchoring (11.4%). Of the error cases, the top five most common initial diagnoses were upper gastrointestinal disease (22.7%), trauma (14.7%), cardiovascular disease (10.9%), respiratory disease (7.5%), and primary headache (6.5%). The corresponding final diagnoses for these errors were intestinal obstruction or peritonitis (27.3%), overlooked traumas (47.4%), other cardiovascular diseases (66.7%), cardiovascular disease (41.4%), and stroke (80%), respectively. Conclusions A comparison of the initial and final diagnoses of cases with diagnostic errors shows that there were more cases with diagnostic errors caused by overlooking another disease in the same organ or a disease in a closely related organ. Supplementary Information The online version contains supplementary material available at 10.1186/s12873-022-00708-3.
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Affiliation(s)
- Kotaro Kunitomo
- Department of General Medicine, Kumamoto Medical Center, Kumamoto, Japan
| | - Taku Harada
- Department of General Medicine, Koto Toyosu Hospital, Tokyo, Japan
| | - Takashi Watari
- General Medicine Center, Shimane University, 89-1, Enya-cho, Izumo shi, Shimane, 693-8501, Japan. .,Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
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