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Faviez C, Chen X, Garcelon N, Zaidan M, Billot K, Petzold F, Faour H, Douillet M, Rozet JM, Cormier-Daire V, Attié-Bitach T, Lyonnet S, Saunier S, Burgun A. Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies. BMC Med Inform Decis Mak 2024; 24:134. [PMID: 38789985 PMCID: PMC11127295 DOI: 10.1186/s12911-024-02538-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/17/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND There are approximately 8,000 different rare diseases that affect roughly 400 million people worldwide. Many of them suffer from delayed diagnosis. Ciliopathies are rare monogenic disorders characterized by a significant phenotypic and genetic heterogeneity that raises an important challenge for clinical diagnosis. Diagnosis support systems (DSS) applied to electronic health record (EHR) data may help identify undiagnosed patients, which is of paramount importance to improve patients' care. Our objective was to evaluate three online-accessible rare disease DSSs using phenotypes derived from EHRs for the diagnosis of ciliopathies. METHODS Two datasets of ciliopathy cases, either proven or suspected, and two datasets of controls were used to evaluate the DSSs. Patient phenotypes were automatically extracted from their EHRs and converted to Human Phenotype Ontology terms. We tested the ability of the DSSs to diagnose cases in contrast to controls based on Orphanet ontology. RESULTS A total of 79 cases and 38 controls were selected. Performances of the DSSs on ciliopathy real world data (best DSS with area under the ROC curve = 0.72) were not as good as published performances on the test set used in the DSS development phase. None of these systems obtained results which could be described as "expert-level". Patients with multisystemic symptoms were generally easier to diagnose than patients with isolated symptoms. Diseases easily confused with ciliopathy generally affected multiple organs and had overlapping phenotypes. Four challenges need to be considered to improve the performances: to make the DSSs interoperable with EHR systems, to validate the performances in real-life settings, to deal with data quality, and to leverage methods and resources for rare and complex diseases. CONCLUSION Our study provides insights into the complexities of diagnosing highly heterogenous rare diseases and offers lessons derived from evaluation existing DSSs in real-world settings. These insights are not only beneficial for ciliopathy diagnosis but also hold relevance for the enhancement of DSS for various complex rare disorders, by guiding the development of more clinically relevant rare disease DSSs, that could support early diagnosis and finally make more patients eligible for treatment.
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Affiliation(s)
- Carole Faviez
- Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France.
- HeKA, Inria Paris, Paris, F-75012, France.
- Universite Paris Cite, Paris, France.
| | - Xiaoyi Chen
- Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France
- HeKA, Inria Paris, Paris, F-75012, France
- Data Science Platform, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, F-75015, France
| | - Nicolas Garcelon
- Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France
- HeKA, Inria Paris, Paris, F-75012, France
- Data Science Platform, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, F-75015, France
| | - Mohamad Zaidan
- Service de Néphrologie, Dialyse et Transplantation, Hôpital Universitaire Bicêtre, Assistance Publique-Hôpitaux de Paris (AP-HP), Kremlin Bicêtre, F-94270, France
| | - Katy Billot
- Laboratory of Renal Hereditary Diseases, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France
| | - Friederike Petzold
- Laboratory of Renal Hereditary Diseases, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France
- Division of Nephrology, University of Leipzig Medical Center, Leipzig, Germany
| | - Hassan Faour
- Data Science Platform, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, F-75015, France
| | - Maxime Douillet
- Data Science Platform, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, F-75015, France
| | - Jean-Michel Rozet
- Laboratory of Genetics in Ophthalmology, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France
| | - Valérie Cormier-Daire
- Reference Centre for Constitutional Bone Diseases, laboratory of Osteochondrodysplasia, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, AP-HP, Paris, F-75015, France
| | - Tania Attié-Bitach
- Service d'Histologie-Embryologie-Cytogénétique, Hôpital Necker-Enfants Malades, AP-HP, Paris, F-75015, France
| | - Stanislas Lyonnet
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, AP-HP, Paris, F-75015, France
- Laboratory of Embryology and Genetics of Congenital Malformations, INSERM UMR 1163, Imagine Institute, Paris Cité, Paris, F-75015, France
| | - Sophie Saunier
- Laboratory of Renal Hereditary Diseases, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France
| | - Anita Burgun
- Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France
- HeKA, Inria Paris, Paris, F-75012, France
- Department of Medical Informatics, Hôpital Necker-Enfants Malades, AP-HP, Paris, F-75015, France
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Udry S, Latino JO, Perez SM, Belizna C, Aranda F, Esteve-Valverde E, Wingeyer SP, Romero DSF, Alijotas-Reig J, de Larrañaga G. Loss of opportunities in the diagnosis and treatment of primary obstetric antiphospholipid syndrome (POAPS): from theory to reality. Clin Rheumatol 2024; 43:1615-1622. [PMID: 38436770 DOI: 10.1007/s10067-023-06846-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/03/2023] [Accepted: 12/12/2023] [Indexed: 03/05/2024]
Abstract
OBJECTIVES (I) To identify and measure the clinical consequences of a delayed diagnosis in patients with primary obstetric antiphospholipid syndrome (POAPS), in terms of time and events associated to antiphospholipid syndrome (APS), and (II) to evaluate the impact of their treatment status on perinatal outcomes, before and after diagnosis. METHODS This retrospective multicentre study included 99 POAPS women who were separated in two groups of timelines based on their diagnostic status: group 1: women who met the clinical criteria for POAPS; group 2: included the same patients from group 1 since they meet the laboratory criteria for APS. In group 1, we assessed the following variables: obstetric events, thrombotic events and time (years) to diagnosis of APS. We also compared perinatal outcomes between patients in group 1 vs. group 2. Women in group 2 were treated with standard of care for POAPS. Simple and multivariable logistic regression analyses were performed. RESULTS Regarding the impact of the delay on diagnosis, a total of 87 APS-related events were recorded: 46 miscarriages, 32 foetal losses and 9 premature deliveries before the 34th week due to preeclampsia, and one thrombosis. The estimated rate of preventable events was 20.58 per year/100 patients. The mean diagnostic delay time was 4.27 years. When we compared both groups during pregnancy, we found that patients in group 1 (no treatment) had a higher association with pregnancy losses [OR = 6.71 (95% CI: 3.59-12.55), p < 0.0001]. CONCLUSION Our findings emphasize the negative impact of POAPS underdiagnosis on patient health and the critical importance of a timely intervention to improve pregnancy outcomes. Key Points •Our study shows the relevance of underdiagnosis on primary obstetric antiphospholipid syndrome (POAPS). •These patients presented a high risk of APS-related events with each passing year. •Shorter diagnostic delay time was observed in the reference centres.
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Affiliation(s)
- Sebastián Udry
- Research Center "Fundación Respirar", Av. Cabildo 1548, C1426AEN, City of Buenos Aires, Argentina
- Autoimmune Thrombophilic Diseases and Pregnancy Section, Acute Hospital "Dr. Carlos G. Durand", Av. DíazVélez 5044, C1405AEN, City of Buenos Aires, Argentina
- Haemostasis and Thrombosis Laboratory, Hospital of Infectious Diseases "Dr, Francisco J. Muñiz", 2272, C1282AEN, UspallataCity of Buenos Aires, Argentina
| | - José O Latino
- Autoimmune Thrombophilic Diseases and Pregnancy Section, Acute Hospital "Dr. Carlos G. Durand", Av. DíazVélez 5044, C1405AEN, City of Buenos Aires, Argentina
| | - Stephanie Morales Perez
- Systemic Autoimmune Disease Unit, Internal Medicine Department, Parc Tauli University Hospital, Sabadell, Spain
- Department of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Cristina Belizna
- Internal Medicine Department Clinique de L'Anjou, 9 Rue de L'Hirondelle, 49000, Angers, France
- Vascular and Coagulation Department, University Hospital Angers, 4 Rue Larrey, 49000, Angers, France
- UMR CNRS 6015, INSERM U1083, University of Angers, Rue Haute de Reculée, 49045, Angers, France
| | - Federico Aranda
- Haemostasis and Thrombosis Laboratory, Hospital of Infectious Diseases "Dr, Francisco J. Muñiz", 2272, C1282AEN, UspallataCity of Buenos Aires, Argentina
| | - Enrique Esteve-Valverde
- Systemic Autoimmune Disease Unit, Internal Medicine Department, Parc Tauli University Hospital, Sabadell, Spain
| | - Silvia Perés Wingeyer
- Haemostasis and Thrombosis Laboratory, Hospital of Infectious Diseases "Dr, Francisco J. Muñiz", 2272, C1282AEN, UspallataCity of Buenos Aires, Argentina
| | - Diego S Fernández Romero
- Autoimmune Thrombophilic Diseases and Pregnancy Section, Acute Hospital "Dr. Carlos G. Durand", Av. DíazVélez 5044, C1405AEN, City of Buenos Aires, Argentina
| | - Jaume Alijotas-Reig
- Department of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain.
- Systemic Autoimmune Disease Unit, Department of Internal Medicine, Vall d'Hebron University Hospital Campus, Barcelona, Spain.
- Vall d'Hebron Research Unit, Vall d'Hebron University Hospital Campus, Barcelona, Spain.
| | - Gabriela de Larrañaga
- Haemostasis and Thrombosis Laboratory, Hospital of Infectious Diseases "Dr, Francisco J. Muñiz", 2272, C1282AEN, UspallataCity of Buenos Aires, Argentina
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Belot A, Benezech S, Tusseau M. A new drug for rare diseases: pozelimab for CHAPLE disease. Lancet 2024; 403:592-593. [PMID: 38278169 DOI: 10.1016/s0140-6736(23)02652-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 11/23/2023] [Indexed: 01/28/2024]
Affiliation(s)
- Alexandre Belot
- Centre International de Recherche en Infectiologie, Inserm U1111, Université Claude Bernard Lyon 1, CNRS UMR5308, ENS de Lyon, Lyon, France; Pediatric Nephrology, Rheumatology, Dermatology, Hôpital Femme Mère Enfant, Hospices Civils de Lyon, Bron 69677, France.
| | - Sarah Benezech
- Centre International de Recherche en Infectiologie, Inserm U1111, Université Claude Bernard Lyon 1, CNRS UMR5308, ENS de Lyon, Lyon, France; Pediatric Immunology, Haematology, Oncology, Hospices Civils de Lyon, Lyon, France
| | - Maud Tusseau
- Centre International de Recherche en Infectiologie, Inserm U1111, Université Claude Bernard Lyon 1, CNRS UMR5308, ENS de Lyon, Lyon, France; Genetic Department, Hôpital Femme Mère Enfant, Hospices Civils de Lyon, Bron, France
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Abdullahi T, Singh R, Eickhoff C. Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models. JMIR MEDICAL EDUCATION 2024; 10:e51391. [PMID: 38349725 PMCID: PMC10900078 DOI: 10.2196/51391] [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: 07/30/2023] [Revised: 11/07/2023] [Accepted: 12/11/2023] [Indexed: 02/15/2024]
Abstract
BACKGROUND Patients with rare and complex diseases often experience delayed diagnoses and misdiagnoses because comprehensive knowledge about these diseases is limited to only a few medical experts. In this context, large language models (LLMs) have emerged as powerful knowledge aggregation tools with applications in clinical decision support and education domains. OBJECTIVE This study aims to explore the potential of 3 popular LLMs, namely Bard (Google LLC), ChatGPT-3.5 (OpenAI), and GPT-4 (OpenAI), in medical education to enhance the diagnosis of rare and complex diseases while investigating the impact of prompt engineering on their performance. METHODS We conducted experiments on publicly available complex and rare cases to achieve these objectives. We implemented various prompt strategies to evaluate the performance of these models using both open-ended and multiple-choice prompts. In addition, we used a majority voting strategy to leverage diverse reasoning paths within language models, aiming to enhance their reliability. Furthermore, we compared their performance with the performance of human respondents and MedAlpaca, a generative LLM specifically designed for medical tasks. RESULTS Notably, all LLMs outperformed the average human consensus and MedAlpaca, with a minimum margin of 5% and 13%, respectively, across all 30 cases from the diagnostic case challenge collection. On the frequently misdiagnosed cases category, Bard tied with MedAlpaca but surpassed the human average consensus by 14%, whereas GPT-4 and ChatGPT-3.5 outperformed MedAlpaca and the human respondents on the moderately often misdiagnosed cases category with minimum accuracy scores of 28% and 11%, respectively. The majority voting strategy, particularly with GPT-4, demonstrated the highest overall score across all cases from the diagnostic complex case collection, surpassing that of other LLMs. On the Medical Information Mart for Intensive Care-III data sets, Bard and GPT-4 achieved the highest diagnostic accuracy scores, with multiple-choice prompts scoring 93%, whereas ChatGPT-3.5 and MedAlpaca scored 73% and 47%, respectively. Furthermore, our results demonstrate that there is no one-size-fits-all prompting approach for improving the performance of LLMs and that a single strategy does not universally apply to all LLMs. CONCLUSIONS Our findings shed light on the diagnostic capabilities of LLMs and the challenges associated with identifying an optimal prompting strategy that aligns with each language model's characteristics and specific task requirements. The significance of prompt engineering is highlighted, providing valuable insights for researchers and practitioners who use these language models for medical training. Furthermore, this study represents a crucial step toward understanding how LLMs can enhance diagnostic reasoning in rare and complex medical cases, paving the way for developing effective educational tools and accurate diagnostic aids to improve patient care and outcomes.
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Affiliation(s)
- Tassallah Abdullahi
- Department of Computer Science, Brown University, Providence, RI, United States
| | - Ritambhara Singh
- Department of Computer Science, Brown University, Providence, RI, United States
- Center for Computational Molecular Biology, Brown University, Providence, RI, United States
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Faviez C, Vincent M, Garcelon N, Boyer O, Knebelmann B, Heidet L, Saunier S, Chen X, Burgun A. Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity. Orphanet J Rare Dis 2024; 19:55. [PMID: 38336713 PMCID: PMC10858490 DOI: 10.1186/s13023-024-03063-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/03/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Rare diseases affect approximately 400 million people worldwide. Many of them suffer from delayed diagnosis. Among them, NPHP1-related renal ciliopathies need to be diagnosed as early as possible as potential treatments have been recently investigated with promising results. Our objective was to develop a supervised machine learning pipeline for the detection of NPHP1 ciliopathy patients from a large number of nephrology patients using electronic health records (EHRs). METHODS AND RESULTS We designed a pipeline combining a phenotyping module re-using unstructured EHR data, a semantic similarity module to address the phenotype dependence, a feature selection step to deal with high dimensionality, an undersampling step to address the class imbalance, and a classification step with multiple train-test split for the small number of rare cases. The pipeline was applied to thirty NPHP1 patients and 7231 controls and achieved good performances (sensitivity 86% with specificity 90%). A qualitative review of the EHRs of 40 misclassified controls showed that 25% had phenotypes belonging to the ciliopathy spectrum, which demonstrates the ability of our system to detect patients with similar conditions. CONCLUSIONS Our pipeline reached very encouraging performance scores for pre-diagnosing ciliopathy patients. The identified patients could then undergo genetic testing. The same data-driven approach can be adapted to other rare diseases facing underdiagnosis challenges.
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Affiliation(s)
- Carole Faviez
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM UMR 1138, 75006, Paris, France.
- Inria, 75012, Paris, France.
| | - Marc Vincent
- Université Paris Cité, Imagine Institute, Data Science Platform, INSERM UMR 1163, 75015, Paris, France
| | - Nicolas Garcelon
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM UMR 1138, 75006, Paris, France
- Inria, 75012, Paris, France
- Université Paris Cité, Imagine Institute, Data Science Platform, INSERM UMR 1163, 75015, Paris, France
| | - Olivia Boyer
- Department of Pediatric Nephrology, APHP-Centre, Reference Center for Inherited Renal Diseases (MARHEA), Imagine Institute, Hôpital Necker-Enfants Malades, Université Paris Cité, 75015, Paris, France
- Laboratory of Renal Hereditary Diseases, INSERM UMR 1163, Imagine Institute, Université Paris Cité, 75015, Paris, France
| | - Bertrand Knebelmann
- Nephrology and Transplantation Department, MARHEA, Hôpital Necker-Enfants Malades, AP-HP, Université Paris Cité, 75015, Paris, France
| | - Laurence Heidet
- Department of Pediatric Nephrology, APHP-Centre, Reference Center for Inherited Renal Diseases (MARHEA), Imagine Institute, Hôpital Necker-Enfants Malades, Université Paris Cité, 75015, Paris, France
| | - Sophie Saunier
- Laboratory of Renal Hereditary Diseases, INSERM UMR 1163, Imagine Institute, Université Paris Cité, 75015, Paris, France
| | - Xiaoyi Chen
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM UMR 1138, 75006, Paris, France
- Inria, 75012, Paris, France
- Université Paris Cité, Imagine Institute, Data Science Platform, INSERM UMR 1163, 75015, Paris, France
| | - Anita Burgun
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM UMR 1138, 75006, Paris, France
- Inria, 75012, Paris, France
- Département d'informatique Médicale, Hôpital Necker-Enfants Malades, AP-HP, 75015, Paris, France
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Yang J, Shu L, Han M, Pan J, Chen L, Yuan T, Tan L, Shu Q, Duan H, Li H. RDmaster: A novel phenotype-oriented dialogue system supporting differential diagnosis of rare disease. Comput Biol Med 2024; 169:107924. [PMID: 38181610 DOI: 10.1016/j.compbiomed.2024.107924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/18/2023] [Accepted: 01/01/2024] [Indexed: 01/07/2024]
Abstract
BACKGROUND Clinicians often lack the necessary expertise to differentially diagnose multiple underlying rare diseases (RDs) due to their complex and overlapping clinical features, leading to misdiagnoses and delayed treatments. The aim of this study is to develop a novel electronic differential diagnostic support system for RDs. METHOD Through integrating two Bayesian diagnostic methods, a candidate list was generated with enhance clinical interpretability for the further Q&A based differential diagnosis (DDX). To achieve an efficient Q&A dialogue strategy, we introduce a novel metric named the adaptive information gain and Gini index (AIGGI) to evaluate the expected gain of interrogated phenotypes within real-time diagnostic states. RESULTS This DDX tool called RDmaster has been implemented as a web-based platform (http://rdmaster.nbscn.org/). A diagnostic trial involving 238 published RD patients revealed that RDmaster outperformed existing RD diagnostic tools, as well as ChatGPT, and was shown to enhance the diagnostic accuracy through its Q&A system. CONCLUSIONS The RDmaster offers an effective multi-omics differential diagnostic technique and outperforms existing tools and popular large language models, particularly enhancing differential diagnosis in collecting diagnostically beneficial phenotypes.
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Affiliation(s)
- Jian Yang
- Clinical Data Center, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China; The College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhejiang, China
| | - Liqi Shu
- Rhode Island Hospital, Warren Alpert Medical School of Brown University, Rhode Island, USA
| | - Mingyu Han
- Neonatal Department, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China
| | - Jiarong Pan
- Neonatal Department, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China
| | - Lihua Chen
- Neonatal Department, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China
| | - Tianming Yuan
- Neonatal Department, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China
| | - Linhua Tan
- Surgical Intensive Care Unit, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China
| | - Qiang Shu
- Clinical Data Center, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China
| | - Huilong Duan
- The College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhejiang, China
| | - Haomin Li
- Clinical Data Center, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China.
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Sellin J, Pantel JT, Börsch N, Conrad R, Mücke M. [Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems]. Schmerz 2024; 38:19-27. [PMID: 38165492 DOI: 10.1007/s00482-023-00777-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Rare diseases are often recognized late. Their diagnosis is particularly challenging due to the diversity, complexity and heterogeneity of clinical symptoms. Computer-aided diagnostic aids, often referred to as diagnostic decision support systems (DDSS), are promising tools for shortening the time to diagnosis. Despite initial positive evaluations, DDSS are not yet widely used, partly due to a lack of integration with existing clinical or practice information systems. OBJECTIVE This article provides an insight into currently existing diagnostic support systems that function without access to electronic patient records and only require information that is easily obtainable. MATERIALS AND METHODS A systematic literature search identified eight articles on DDSS that can assist in the diagnosis of rare diseases with no need for access to electronic patient records or other information systems in practices and hospitals. The main advantages and disadvantages of the identified rare disease diagnostic support systems were extracted and summarized. RESULTS Symptom checkers and DDSS based on portrait photos and pain drawings already exist. The degree of maturity of these applications varies. CONCLUSION DDSS currently still face a number of challenges, such as concerns about data protection and accuracy, and acceptance and awareness continue to be rather low. On the other hand, there is great potential for faster diagnosis, especially for rare diseases, which are easily overlooked due to their large number and the low awareness of them. The use of DDSS should therefore be carefully considered by doctors on a case-by-case basis.
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Affiliation(s)
- Julia Sellin
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland.
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland.
| | - Jean Tori Pantel
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
| | - Natalie Börsch
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
| | - Rupert Conrad
- Klinik für Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Münster, Münster, Deutschland
| | - Martin Mücke
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
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Völkel L, Wagner AD. [Faster diagnosis of rare diseases with artificial intelligence-A precept of ethics, economy and quality of life]. INNERE MEDIZIN (HEIDELBERG, GERMANY) 2023; 64:1033-1040. [PMID: 37861723 PMCID: PMC10602953 DOI: 10.1007/s00108-023-01599-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/14/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND Approximately 300 million people worldwide suffer from a rare disease. An optimal treatment requires a successful diagnosis. This takes a particularly long time, especially for rare diseases. Digital diagnosis support systems could be important aids in accelerating a successful diagnosis in the future. OBJECTIVE The current possibilities of digital diagnostic support systems in the diagnosis of rare diseases and questions that still need to be clarified are presented in relation to the parameters of ethics, economy and quality of life. MATERIAL AND METHODS Current research results of the authors were compiled and discussed in the context of the current literature. A case study is used to illustrate the potential of digital diagnostic support systems. RESULTS Digital diagnostic support systems and experts together can accelerate the successful diagnosis in patients with rare diseases. This could have a positive impact on patients' quality of life and lead to potential savings in direct and indirect costs in the healthcare system. CONCLUSION Ensuring data security, legal certainty and functionality in the use of digital diagnostic support systems is of great importance in order to create trust among experts and patients. Continuous further development of the systems by means of artificial intelligence (AI) could also enable patients to accelerate diagnosis in the future.
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Affiliation(s)
- Lukas Völkel
- Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.
| | - Annette D Wagner
- Abteilung für Nieren- und Hochdruckerkrankungen, Ambulanz für seltene entzündliche Systemerkrankungen mit Nierenbeteiligung, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.
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Wojtara M, Rana E, Rahman T, Khanna P, Singh H. Artificial intelligence in rare disease diagnosis and treatment. Clin Transl Sci 2023; 16:2106-2111. [PMID: 37646577 PMCID: PMC10651639 DOI: 10.1111/cts.13619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/30/2023] [Accepted: 08/13/2023] [Indexed: 09/01/2023] Open
Abstract
Artificial intelligence (AI) utilization in health care has grown over the past few years. It also has demonstrated potential in improving the efficiency of diagnosis and treatment. Some types of AI, such as machine learning, allow for the efficient analysis of vast datasets, identifying patterns, and generating key insights. Predictions can then be made for medical diagnosis and personalized treatment recommendations. The use of AI can bypass some conventional limitations associated with rare diseases. Namely, it can optimize traditional randomized control trials, and may eventually reduce costs for drug research and development. Recent advancements have enabled researchers to train models based on large datasets and then fine-tune these models on smaller datasets typically associated with rare diseases. In this mini-review, we discuss recent advancements in AI and how AI can be applied to streamline rare disease diagnosis and optimize treatment.
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Affiliation(s)
- Magda Wojtara
- Department of Human GeneticsUniversity of MichiganAnn ArborMichiganUSA
| | - Emaan Rana
- Department of ScienceUniversity of Western OntarioLondonOntarioCanada
| | - Taibia Rahman
- Department of MedicineDavid Tvildiani Medical UniversityTbilisiGeorgia
| | - Palak Khanna
- Department of MedicineIvane Javakhishvili Tbilisi State UniversityTbilisiGeorgia
| | - Heshwin Singh
- Department of BiologyStony Brook UniversityStony BrookNew YorkUSA
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10
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Ruffatti A, Tonello M, Calligaro A, Del Ross T, Favaro M, Zen M, Hoxha A, Alaibac M. Prevalence and adverse consequences of delayed diagnosis and misdiagnosis in thrombotic antiphospholipid syndrome. An observational cohort study and a review of the literature. Clin Rheumatol 2023; 42:3007-3019. [PMID: 37453028 PMCID: PMC10587197 DOI: 10.1007/s10067-023-06699-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
OBIECTIVES This study aims to prospectively evaluate the frequency and adverse consequences of diagnostic delay and misdiagnosis in a cohort of patients with thrombotic antiphospholipid syndrome (TAPS). In addition, a systematic review of the literature concerning the diagnostic delay and misdiagnosis of TAPS was carried out. METHODS Patient enrollment occurred between 1999 and 2022. The study group was formed by TAPS patients whose diagnosis was delayed and those who were misdiagnosed. The control group was made up of patients who were timely and correctly diagnosed with TAPS. RESULTS The literature review showed 42 misdiagnosed patients, 27 of them were in one retrospective cohort study and 15 in 13 case reports. One hundred sixty-one out of 189 patients (85.2%) received a timely, correct diagnosis of TAPS; 28 (14.8%) did not. The number of patients with diagnostic issues was significantly higher for the first period (1999-2010), and the number of patients with a correct diagnosis was significantly higher for the second one (2011-2022). When the clinical and laboratory characteristics of the patients with delayed diagnosis were compared with those with misdiagnosis, there was a significantly higher number of severe adverse consequences characterized by permanent disability or death in the latter group. The two most common types of misdiagnoses were systemic lupus erythematosus (6 cases, 46.1%) and cardiovascular diseases (4 cases, 30.8%). CONCLUSIONS The study demonstrates that although knowledge about TAPS has improved over time, diagnostic delays and errors remains to be addressed as they are strongly associated to adverse consequences. Key Points •Although knowledge of thrombotic antiphospholipid syndrome has improved over time, it is still limited. •Diagnostic delay and misdiagnosis are still an important issue that remains to be addressed as they are strongly associated to adverse consequences. •The three more frequent misdiagnoses are multiple sclerosis, systemic lupus erythematosus and cardiovascular diseases.
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Affiliation(s)
- Amelia Ruffatti
- Department of Medicine-DIMED, University Hospital of Padua, Padua, Italy.
| | - Marta Tonello
- Department of Medicine-DIMED, Rheumatology Unit, University Hospital of Padua, Padua, Italy
| | - Antonia Calligaro
- Department of Medicine-DIMED, Rheumatology Unit, University Hospital of Padua, Padua, Italy
| | - Teresa Del Ross
- Department of Medicine-DIMED, Rheumatology Unit, University Hospital of Padua, Padua, Italy
| | - Maria Favaro
- Department of Medicine-DIMED, Rheumatology Unit, University Hospital of Padua, Padua, Italy
| | - Margherita Zen
- Department of Medicine-DIMED, Rheumatology Unit, University Hospital of Padua, Padua, Italy
| | - Ariela Hoxha
- Department of Medicine-DIMED, General Internal Medicine Unit, Thrombotic and Hemorrhagic Disease Unit, University Hospital of Padua, Padua, Italy
| | - Mauro Alaibac
- Department of Medicine-DIMED, Dermatology Unit, University Hospital of Padua, Padua, Italy
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11
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Tinker RJ, Peterson J, Bastarache L. Phenotypic presentation of Mendelian disease across the diagnostic trajectory in electronic health records. Genet Med 2023; 25:100921. [PMID: 37337966 PMCID: PMC11092403 DOI: 10.1016/j.gim.2023.100921] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 06/12/2023] [Accepted: 06/13/2023] [Indexed: 06/21/2023] Open
Abstract
PURPOSE To investigate the phenotypic presentation of Mendelian disease across the diagnostic trajectory in the electronic health record (EHR). METHODS We applied a conceptual model to delineate the diagnostic trajectory of Mendelian disease to the EHRs of patients affected by 1 of 9 Mendelian diseases. We assessed data availability and phenotype ascertainment across the diagnostic trajectory using phenotype risk scores and validated our findings via chart review of patients with hereditary connective tissue disorders. RESULTS We identified 896 individuals with genetically confirmed diagnoses, 216 (24%) of whom had fully ascertained diagnostic trajectories. Phenotype risk scores increased following clinical suspicion and diagnosis (P < 1 × 10-4, Wilcoxon rank sum test). We found that of all International Classification of Disease-based phenotypes in the EHR, 66% were recorded after clinical suspicion, and manual chart review yielded consistent results. CONCLUSION Using a novel conceptual model to study the diagnostic trajectory of genetic disease in the EHR, we demonstrated that phenotype ascertainment is, in large part, driven by the clinical examinations and studies prompted by clinical suspicion of a genetic disease, a process we term diagnostic convergence. Algorithms designed to detect undiagnosed genetic disease should consider censoring EHR data at the first date of clinical suspicion to avoid data leakage.
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Affiliation(s)
- Rory J Tinker
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Josh Peterson
- Vanderbilt University Medical Center, Department of Medicine, Nashville, TN; Vanderbilt University Medical Center, Department of Biomedical Informatics, Nashville, TN
| | - Lisa Bastarache
- Vanderbilt University Medical Center, Department of Biomedical Informatics, Nashville, TN.
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12
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Ohta R, Sano C. Utilizing Learn-to-Rank Systems for More Effective Diagnosis in Rural Family Medicine. Cureus 2023; 15:e47219. [PMID: 38022090 PMCID: PMC10653340 DOI: 10.7759/cureus.47219] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
This editorial investigates the development and efficacy of Japanese learn-to-rank approach systems in family medicine, emphasizing their establishment by Dr. Keijiro Torigoe and their significance in rural community hospitals. Initiated in 1977, Dr. Torigoe's innovative system integrated international medical knowledge with technology, yielding a comprehensive database of 7,000 registered diseases. These learn-to-rank approaches, notably the listwise method, address technological gaps in extracting data on differential diseases and enhance the predictive performance of clinical decision support systems, offering a holistic, culturally resonant healthcare approach. They are especially vital in rural medicine, aiding in managing the volatility, uncertainty, complexity, and ambiguity prevalent among older patients, streamlining diagnoses, and improving healthcare delivery in resource-constrained settings. In conclusion, integrating Japanese learn-to-rank approach systems is pivotal in revolutionizing disease diagnosis, catering to diverse rural health needs, and fostering sustainability in rural healthcare systems. By harmonizing medical insights with innovation, they demonstrate the potential for a comprehensive and contextually relevant approach to healthcare in Japan.
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Affiliation(s)
| | - Chiaki Sano
- Community Medicine Management, Shimane University Faculty of Medicine, Izumo, JPN
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13
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Abdallah S, Sharifa M, I Kh Almadhoun MK, Khawar MM, Shaikh U, Balabel KM, Saleh I, Manzoor A, Mandal AK, Ekomwereren O, Khine WM, Oyelaja OT. The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders. Cureus 2023; 15:e46860. [PMID: 37954711 PMCID: PMC10636514 DOI: 10.7759/cureus.46860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2023] [Indexed: 11/14/2023] Open
Abstract
Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic complexities, present significant challenges to healthcare systems. This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. It emphasizes the need for accurate and early diagnosis of RDs, often hindered by genetic and clinical heterogeneity. This article discusses how AI and ML are reshaping healthcare, providing examples of their effectiveness in disease diagnosis, prognosis, image analysis, and drug repurposing. It highlights AI's ability to efficiently analyze extensive datasets and expedite diagnosis, showcasing case studies like Face2Gene. Furthermore, the article explores how AI tailors treatment plans for RDs, leveraging ML and deep learning (DL) to create personalized therapeutic regimens. It emphasizes AI's role in drug discovery, including the identification of potential candidates for rare disease treatments. Challenges and limitations related to AI in healthcare, including ethical, legal, technical, and human aspects, are addressed. This article underscores the importance of data ethics, privacy, and algorithmic fairness, as well as the need for standardized evaluation techniques and transparency in AI research. It highlights second-generation AI systems that prioritize patient-centric care, efficient patient recruitment for clinical trials, and the significance of high-quality data. The integration of AI with telemedicine, the growth of health databases, and the potential for personalized therapeutic recommendations are identified as promising directions for the field. In summary, this article provides a comprehensive exploration of how AI and ML are revolutionizing the diagnosis and treatment of RDs, addressing challenges while considering ethical implications in this rapidly evolving healthcare landscape.
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Affiliation(s)
- Shenouda Abdallah
- Surgery, Jaber Al Ahmad Al Jaber Al Sabah Hospital, Kuwait City, KWT
| | | | | | | | - Unzla Shaikh
- Internal Medicine, Liaquat University of Medical and Health Sciences, Hyderabad, PAK
| | | | - Inam Saleh
- Pediatrics, University of Kentucky College of Medicine, Lexington, USA
| | - Amima Manzoor
- Internal Medicine, Jinnah Sindh Medical University, Karachi, PAK
| | - Arun Kumar Mandal
- General Medicine, Mahawai Basic Hospital/The Oda Foundation, Kalikot, NPL
- Medicine, Manipal College of Medical Sciences, Pokhara, NPL
| | - Osatohanmwen Ekomwereren
- Trauma and Orthopaedics, Royal Shrewsbury Hospital, Shrewsbury and Telford Hospital NHS Trust, Shrewsbury, GBR
| | - Wai Mon Khine
- Internal Medicine, Caribbean Medical School, St. Georges, GRD
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14
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Raycheva R, Kostadinov K, Mitova E, Bogoeva N, Iskrov G, Stefanov G, Stefanov R. Challenges in mapping European rare disease databases, relevant for ML-based screening technologies in terms of organizational, FAIR and legal principles: scoping review. Front Public Health 2023; 11:1214766. [PMID: 37780450 PMCID: PMC10540868 DOI: 10.3389/fpubh.2023.1214766] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 08/30/2023] [Indexed: 10/03/2023] Open
Abstract
Background Given the increased availability of data sources such as hospital information systems, electronic health records, and health-related registries, a novel approach is required to develop artificial intelligence-based decision support that can assist clinicians in their diagnostic decision-making and shorten rare disease patients' diagnostic odyssey. The aim is to identify key challenges in the process of mapping European rare disease databases, relevant to ML-based screening technologies in terms of organizational, FAIR and legal principles. Methods A scoping review was conducted based on the PRISMA-ScR checklist. The primary article search was conducted in three electronic databases (MEDLINE/Pubmed, Scopus, and Web of Science) and a secondary search was performed in Google scholar and on the organizations' websites. Each step of this review was carried out independently by two researchers. A charting form for relevant study analysis was developed and used to categorize data and identify data items in three domains - organizational, FAIR and legal. Results At the end of the screening process, 73 studies were eligible for review based on inclusion and exclusion criteria with more than 60% (n = 46) of the research published in the last 5 years and originated only from EU/EEA countries. Over the ten-year period (2013-2022), there is a clear cycling trend in the publications, with a peak of challenges reporting every four years. Within this trend, the following dynamic was identified: except for 2016, organizational challenges dominated the articles published up to 2018; legal challenges were the most frequently discussed topic from 2018 to 2022. The following distribution of the data items by domains was observed - (1) organizational (n = 36): data accessibility and sharing (20.2%); long-term sustainability (18.2%); governance, planning and design (17.2%); lack of harmonization and standardization (17.2%); quality of data collection (16.2%); and privacy risks and small sample size (11.1%); (2) FAIR (n = 15): findable (17.9%); accessible sustainability (25.0%); interoperable (39.3%); and reusable (17.9%); and (3) legal (n = 33): data protection by all means (34.4%); data management and ownership (22.9%); research under GDPR and member state law (20.8%); trust and transparency (13.5%); and digitalization of health (8.3%). We observed a specific pattern repeated in all domains during the process of data charting and data item identification - in addition to the outlined challenges, good practices, guidelines, and recommendations were also discussed. The proportion of publications addressing only good practices, guidelines, and recommendations for overcoming challenges when mapping RD databases in at least one domain was calculated to be 47.9% (n = 35). Conclusion Despite the opportunities provided by innovation - automation, electronic health records, hospital-based information systems, biobanks, rare disease registries and European Reference Networks - the results of the current scoping review demonstrate a diversity of the challenges that must still be addressed, with immediate actions on ensuring better governance of rare disease registries, implementing FAIR principles, and enhancing the EU legal framework.
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Affiliation(s)
- Ralitsa Raycheva
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Kostadin Kostadinov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Elena Mitova
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Nataliya Bogoeva
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Georgi Iskrov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Georgi Stefanov
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Rumen Stefanov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
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15
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Stirnemann JJ, Besson R, Spaggiari E, Rojo S, Loge F, Peyro-Saint-Paul H, Allassonniere S, Le Pennec E, Hutchinson C, Sebire N, Ville Y. Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 62:353-360. [PMID: 37161503 DOI: 10.1002/uog.26242] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 02/13/2023] [Accepted: 03/20/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVE Prenatal diagnosis of a rare disease on ultrasound relies on a physician's ability to remember an intractable amount of knowledge. We developed a real-time decision support system (DSS) that suggests, at each step of the examination, the next phenotypic feature to assess, optimizing the diagnostic pathway to the smallest number of possible diagnoses. The objective of this study was to evaluate the performance of this real-time DSS using clinical data. METHODS This validation study was conducted on a database of 549 perinatal phenotypes collected from two referral centers (one in France and one in the UK). Inclusion criteria were: at least one anomaly was visible on fetal ultrasound after 11 weeks' gestation; the anomaly was confirmed postnatally; an associated rare disease was confirmed or ruled out based on postnatal/postmortem investigation, including physical examination, genetic testing and imaging; and, when confirmed, the syndrome was known by the DSS software. The cases were assessed retrospectively by the software, using either the full phenotype as a single input, or a stepwise input of phenotypic features, as prompted by the software, mimicking its use in a real-life clinical setting. Adjudication of discordant cases, in which there was disagreement between the DSS output and the postnatally confirmed ('ascertained') diagnosis, was performed by a panel of external experts. The proportion of ascertained diagnoses within the software's top-10 differential diagnoses output was evaluated, as well as the sensitivity and specificity of the software to select correctly as its best guess a syndromic or isolated condition. RESULTS The dataset covered 110/408 (27%) diagnoses within the software's database, yielding a cumulative prevalence of 83%. For syndromic cases, the ascertained diagnosis was within the top-10 list in 93% and 83% of cases using the full-phenotype and stepwise input, respectively, after adjudication. The full-phenotype and stepwise approaches were associated, respectively, with a specificity of 94% and 96% and a sensitivity of 99% and 84%. The stepwise approach required an average of 13 queries to reach the final set of diagnoses. CONCLUSIONS The DSS showed high performance when applied to real-world data. This validation study suggests that such software can improve perinatal care, efficiently providing complex and otherwise overlooked knowledge to care-providers involved in ultrasound-based prenatal diagnosis. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- J J Stirnemann
- Department of Obstetrics and Maternal-Fetal Medicine, Necker-Enfants Malades Hospital, AP-HP, Paris, France
- EA7328 Université de Paris, IMAGINE Institute, Paris, France
| | | | - E Spaggiari
- Department of Obstetrics and Maternal-Fetal Medicine, Necker-Enfants Malades Hospital, AP-HP, Paris, France
- EA7328 Université de Paris, IMAGINE Institute, Paris, France
- Department of Histology-Embryology and Cytogenetics, Unit of Embryo and Fetal Pathology, Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | | | | | | | - S Allassonniere
- School of Medicine, Université de Paris, INRIA EPI HEKA, INSERM UMR 1138, Sorbonne Université, Paris, France
- Center for Applied Mathematics, Ecole Polytechnique, Institut Polytechnique de Paris, Paris, France
| | - E Le Pennec
- Center for Applied Mathematics, Ecole Polytechnique, Institut Polytechnique de Paris, Paris, France
- Xpop, INRIA Saclay Center, Paris, France
| | - C Hutchinson
- NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
| | - N Sebire
- NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
| | - Y Ville
- Department of Obstetrics and Maternal-Fetal Medicine, Necker-Enfants Malades Hospital, AP-HP, Paris, France
- EA7328 Université de Paris, IMAGINE Institute, Paris, France
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16
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Donadio D, Terry SF. The Application of Artificial Intelligence in the Diagnosis of Cancer and Rare Genetic Diseases. Genet Test Mol Biomarkers 2023; 27:203-204. [PMID: 37471205 DOI: 10.1089/gtmb.2023.29074.persp] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023] Open
Affiliation(s)
- Danielle Donadio
- Clemson University, Clemson, South Carolina, USA
- Genetic Alliance, Damascus, Maryland, USA
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17
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Michalski AA, Lis K, Stankiewicz J, Kloska SM, Sycz A, Dudziński M, Muras-Szwedziak K, Nowicki M, Bazan-Socha S, Dabrowski MJ, Basak GW. Supporting the Diagnosis of Fabry Disease Using a Natural Language Processing-Based Approach. J Clin Med 2023; 12:jcm12103599. [PMID: 37240705 DOI: 10.3390/jcm12103599] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/01/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023] Open
Abstract
In clinical practice, the consideration of non-specific symptoms of rare diseases in order to make a correct and timely diagnosis is often challenging. To support physicians, we developed a decision-support scoring system on the basis of retrospective research. Based on the literature and expert knowledge, we identified clinical features typical for Fabry disease (FD). Natural language processing (NLP) was used to evaluate patients' electronic health records (EHRs) to obtain detailed information about FD-specific patient characteristics. The NLP-determined elements, laboratory test results, and ICD-10 codes were transformed and grouped into pre-defined FD-specific clinical features that were scored in the context of their significance in the FD signs. The sum of clinical feature scores constituted the FD risk score. Then, medical records of patients with the highest FD risk score were reviewed by physicians who decided whether to refer a patient for additional tests or not. One patient who obtained a high-FD risk score was referred for DBS assay and confirmed to have FD. The presented NLP-based, decision-support scoring system achieved AUC of 0.998, which demonstrates that the applied approach enables for accurate identification of FD-suspected patients, with a high discrimination power.
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Affiliation(s)
- Adrian A Michalski
- Saventic Health, Polna 66/12 Street, 87-100 Torun, Poland
- Department of Analytical Chemistry, Nicolaus Copernicus University Ludwik Rydygier Collegium Medicum, 85-089 Bydgoszcz, Poland
| | - Karol Lis
- Saventic Health, Polna 66/12 Street, 87-100 Torun, Poland
- Department of Hematology, Transplantation and Internal Medicine, Medical University of Warsaw, 02-097 Warsaw, Poland
| | - Joanna Stankiewicz
- Saventic Health, Polna 66/12 Street, 87-100 Torun, Poland
- Department of Pediatrics, Hematology and Oncology, Nicolaus Copernicus University Ludwik Rydygier Collegium Medicum, 85-094 Bydgoszcz, Poland
| | - Sylwester M Kloska
- Saventic Health, Polna 66/12 Street, 87-100 Torun, Poland
- Department of Forensic Medicine, Nicolaus Copernicus University Ludwik Rydygier Collegium Medicum, 85-067 Bydgoszcz, Poland
| | - Arkadiusz Sycz
- Saventic Health, Polna 66/12 Street, 87-100 Torun, Poland
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland
| | - Marek Dudziński
- Saventic Health, Polna 66/12 Street, 87-100 Torun, Poland
- Department of Hematology, Institute of Medical Sciences, College of Medical Sciences, University of Rzeszow, 35-959 Rzeszow, Poland
| | - Katarzyna Muras-Szwedziak
- Saventic Foundation, Polna 66/12 Street, 87-100 Torun, Poland
- Department of Nephrology, Hypertension and Kidney Transplantation, Medical University of Lodz, 90-419 Lodz, Poland
| | - Michał Nowicki
- Saventic Foundation, Polna 66/12 Street, 87-100 Torun, Poland
- Department of Nephrology, Hypertension and Kidney Transplantation, Medical University of Lodz, 90-419 Lodz, Poland
| | - Stanisława Bazan-Socha
- Saventic Foundation, Polna 66/12 Street, 87-100 Torun, Poland
- Department of Internal Medicine, Faculty of Medicine, Jagiellonian University Medical College, 31-008 Krakow, Poland
| | - Michal J Dabrowski
- Saventic Health, Polna 66/12 Street, 87-100 Torun, Poland
- Computational Biology Group, Institute of Computer Science of the Polish Academy of Sciences, 01-248 Warsaw, Poland
| | - Grzegorz W Basak
- Saventic Health, Polna 66/12 Street, 87-100 Torun, Poland
- Department of Hematology, Transplantation and Internal Medicine, Medical University of Warsaw, 02-097 Warsaw, Poland
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18
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Emmert D, Szczypien N, Bender TTA, Grigull L, Gass A, Link C, Klawonn F, Conrad R, Mücke M, Sellin J. A diagnostic support system based on pain drawings: binary and k-disease classification of EDS, GBS, FSHD, PROMM, and a control group with Pain2D. Orphanet J Rare Dis 2023; 18:70. [PMID: 36978184 PMCID: PMC10053427 DOI: 10.1186/s13023-023-02663-z] [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: 02/14/2022] [Accepted: 03/11/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND AND OBJECTIVE The diagnosis of rare diseases (RDs) is often challenging due to their rarity, variability and the high number of individual RDs, resulting in a delay in diagnosis with adverse effects for patients and healthcare systems. The development of computer assisted diagnostic decision support systems could help to improve these problems by supporting differential diagnosis and by prompting physicians to initiate the right diagnostic tests. Towards this end, we developed, trained and tested a machine learning model implemented as part of the software called Pain2D to classify four rare diseases (EDS, GBS, FSHD and PROMM), as well as a control group of unspecific chronic pain, from pen-and-paper pain drawings filled in by patients. METHODS Pain drawings (PDs) were collected from patients suffering from one of the four RDs, or from unspecific chronic pain. The latter PDs were used as an outgroup in order to test how Pain2D handles more common pain causes. A total of 262 (59 EDS, 29 GBS, 35 FSHD, 89 PROMM, 50 unspecific chronic pain) PDs were collected and used to generate disease specific pain profiles. PDs were then classified by Pain2D in a leave-one-out-cross-validation approach. RESULTS Pain2D was able to classify the four rare diseases with an accuracy of 61-77% with its binary classifier. EDS, GBS and FSHD were classified correctly by the Pain2D k-disease classifier with sensitivities between 63 and 86% and specificities between 81 and 89%. For PROMM, the k-disease classifier achieved a sensitivity of 51% and specificity of 90%. CONCLUSIONS Pain2D is a scalable, open-source tool that could potentially be trained for all diseases presenting with pain.
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Affiliation(s)
- D Emmert
- Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany
- Institute for Virology, University Hospital Bonn, Bonn, Germany
| | - N Szczypien
- Institute for Information Engineering, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
- Biostatistics Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Tim T A Bender
- Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany
| | - L Grigull
- Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany
| | - A Gass
- Clinic for Anesthesiology and Operative Intensive Care Medicine, Department of Pain Medicine, University Hospital Bonn, Bonn, Germany
| | - C Link
- Clinic for Anesthesiology and Operative Intensive Care Medicine, Department of Pain Medicine, University Hospital Bonn, Bonn, Germany
| | - F Klawonn
- Institute for Information Engineering, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
- Biostatistics Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - R Conrad
- Department of Psychosomatic Medicine and Psychotherapy, University Hospital Muenster, Muenster, Germany.
| | - M Mücke
- Institute for Digitalization and General Medicine, University Hospital RWTH Aachen, Aachen, Germany.
- Center for Rare Diseases Aachen (ZSEA), University Hospital RWTH Aachen, Aachen, Germany.
| | - J Sellin
- Institute for Digitalization and General Medicine, University Hospital RWTH Aachen, Aachen, Germany.
- Center for Rare Diseases Aachen (ZSEA), University Hospital RWTH Aachen, Aachen, Germany.
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19
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Tinker RJ, Peterson J, Bastarache L. Phenotypic convergence: a novel phenomenon in the diagnostic process of Mendelian genetic disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.17.23284691. [PMID: 36711865 PMCID: PMC9882467 DOI: 10.1101/2023.01.17.23284691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Introduction The study of Mendelian disease has yielded a large body of knowledge about the phenotypic presentation of disease. Less is known about the way the diseases are reflected in the electronic health record (EHR). Aim To develop an EHR-based model of the diagnostic trajectory and investigate data availability and the longitudinal distribution of signs and symptoms of a Mendelian disorder within EHRs. Methods We created a conceptual model to specify key time points of the diagnostic trajectory and applied it to individuals with genetically confirmed hereditary connective tissue diseases (HCTD). Using the model, we assessed EHR data availability within each time interval. We tested the performance of phenotype risk scores (PheRS), an algorithm that detects Mendelian disease patterns and assessed the phenotypic expression of HCTD over the diagnostic trajectory. Results We identified 251 individuals with HCTD; 79 (35%) of these patients had a fully ascertained diagnostic trajectory. There were few documented signs and symptoms prior to clinical suspicion that evoked an HCTD disorder (median PheRS 0.14); once suspicion was documented, median PheRS increased to 1.87 (SD). The majority (72%) of phenotypic features were identified post clinical suspicion. Discussion Using a novel conceptual model for the diagnostic trajectory of Mendelian disease, we demonstrated that phenotype ascertainment is, in part, driven by the diagnostic process and that many findings are only documented following clinical suspicion and diagnosis, a process we term phenotypic convergence. Therefore, algorithms that aim to detect undiagnosed Mendelian disease should censor EHR data to avoid data leakage.
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Lin S, Nateqi J, Weingartner-Ortner R, Gruarin S, Marling H, Pilgram V, Lagler FB, Aigner E, Martin AG. An artificial intelligence-based approach for identifying rare disease patients using retrospective electronic health records applied for Pompe disease. Front Neurol 2023; 14:1108222. [PMID: 37153672 PMCID: PMC10160659 DOI: 10.3389/fneur.2023.1108222] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 04/03/2023] [Indexed: 05/10/2023] Open
Abstract
Objective We retrospectively screened 350,116 electronic health records (EHRs) to identify suspected patients for Pompe disease. Using these suspected patients, we then describe their phenotypical characteristics and estimate the prevalence in the respective population covered by the EHRs. Methods We applied Symptoma's Artificial Intelligence-based approach for identifying rare disease patients to retrospective anonymized EHRs provided by the "University Hospital Salzburg" clinic group. Within 1 month, the AI screened 350,116 EHRs reaching back 15 years from five hospitals, and 104 patients were flagged as probable for Pompe disease. Flagged patients were manually reviewed and assessed by generalist and specialist physicians for their likelihood for Pompe disease, from which the performance of the algorithms was evaluated. Results Of the 104 patients flagged by the algorithms, generalist physicians found five "diagnosed," 10 "suspected," and seven patients with "reduced suspicion." After feedback from Pompe disease specialist physicians, 19 patients remained clinically plausible for Pompe disease, resulting in a specificity of 18.27% for the AI. Estimating from the remaining plausible patients, the prevalence of Pompe disease for the greater Salzburg region [incl. Bavaria (Germany), Styria (Austria), and Upper Austria (Austria)] was one in every 18,427 people. Phenotypes for patient cohorts with an approximated onset of symptoms above or below 1 year of age were established, which correspond to infantile-onset Pompe disease (IOPD) and late-onset Pompe disease (LOPD), respectively. Conclusion Our study shows the feasibility of Symptoma's AI-based approach for identifying rare disease patients using retrospective EHRs. Via the algorithm's screening of an entire EHR population, a physician had only to manually review 5.47 patients on average to find one suspected candidate. This efficiency is crucial as Pompe disease, while rare, is a progressively debilitating but treatable neuromuscular disease. As such, we demonstrated both the efficiency of the approach and the potential of a scalable solution to the systematic identification of rare disease patients. Thus, similar implementation of this methodology should be encouraged to improve care for all rare disease patients.
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Affiliation(s)
- Simon Lin
- Science Department, Symptoma GmbH, Vienna, Austria
- Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Jama Nateqi
- Science Department, Symptoma GmbH, Vienna, Austria
- Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
| | | | | | | | - Vinzenz Pilgram
- Medical and Information Technology - MIT, University Hospital Salzburg (SALK), Salzburg, Austria
| | - Florian B. Lagler
- Medical and Information Technology - MIT, University Hospital Salzburg (SALK), Salzburg, Austria
- Department of Pediatrics and Institute for Inherited Metabolic Diseases, Paracelsus Medical University, Salzburg, Austria
| | - Elmar Aigner
- Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
- Medical and Information Technology - MIT, University Hospital Salzburg (SALK), Salzburg, Austria
| | - Alistair G. Martin
- Science Department, Symptoma GmbH, Vienna, Austria
- *Correspondence: Alistair G. Martin
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Benito-Lozano J, López-Villalba B, Arias-Merino G, Posada de la Paz M, Alonso-Ferreira V. Diagnostic delay in rare diseases: data from the Spanish rare diseases patient registry. Orphanet J Rare Dis 2022; 17:418. [PMID: 36397119 PMCID: PMC9670379 DOI: 10.1186/s13023-022-02530-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 09/30/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND According to the International Rare Diseases Research Consortium (IRDiRC), a known rare disease (RD) should be diagnosable within a year. This study sought: firstly, to ascertain how long it takes to obtain the diagnosis of a RD in Spain, along with its associated time trend; and secondly, to identify and measure diagnostic delay (defined by the IRDiRC as any period exceeding a year) by reference to the characteristics of RDs and the persons affected by them. METHODS Using data sourced from the Spanish Rare Diseases Patient Registry, we performed a descriptive analysis of the time elapsed between symptom onset and diagnosis of each RD, by sex, age and date of symptom onset, and type of RD. We analysed the time trend across the period 1960-2021 and possible change points, using a Joinpoint regression model and assuming a Poisson distribution. The multivariate analysis was completed with backward stepwise logistic regression. RESULTS Detailed information was obtained on 3304 persons with RDs: 56.4% had experienced delay in diagnosis of their RDs, with the mean time taken being 6.18 years (median = 2; IQR 0.2-7.5). Both the percentage of patients with diagnostic delay and the average time to diagnosis underwent a significant reduction across the study period (p < 0.001). There was a higher percentage of diagnostic delays: in women (OR 1.25; 95% CI 1.07-1.45); in cases with symptom onset at age 30-44 years (OR 1.48; 95% CI 1.19-1.84): and when analysed by type of RD, in mental and behavioural disorders (OR 4.21; 95% CI 2.26-7.85), followed by RDs of the nervous system (OR 1.39; 95% CI 1.02-1.88). CONCLUSIONS This is the first study to quantify time to diagnosis of RDs in Spain, based on data from a national registry open to any RD. Since over half of all persons affected by RDs experience delay in diagnosis, new studies are needed to ascertain the factors associated with this delay and the implications this has on the lives of patients and their families.
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Affiliation(s)
- Juan Benito-Lozano
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain ,grid.10702.340000 0001 2308 8920Universidad Nacional de Educación a Distancia (UNED), 28015 Madrid, Spain
| | - Blanca López-Villalba
- grid.411057.60000 0000 9274 367XPreventive Medicine and Public Health, Hospital Clínico Universitario de Valladolid (HCUV), 47003 Valladolid, Spain
| | - Greta Arias-Merino
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Manuel Posada de la Paz
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Verónica Alonso-Ferreira
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain ,grid.413448.e0000 0000 9314 1427Centre for Biomedical Network Research On Rare Diseases (CIBERER), Instituto de Salud Carlos III, 28029 Madrid, Spain
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22
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Cordier BA, Sawaya NPD, Guerreschi GG, McWeeney SK. Biology and medicine in the landscape of quantum advantages. J R Soc Interface 2022; 19:20220541. [PMID: 36448288 PMCID: PMC9709576 DOI: 10.1098/rsif.2022.0541] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.
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Affiliation(s)
- Benjamin A. Cordier
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97202, USA
| | | | | | - Shannon K. McWeeney
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97202, USA,Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97202, USA,Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR 97202, USA
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23
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Wu DW, Bernstein JA, Bejerano G. Discovering monogenic patients with a confirmed molecular diagnosis in millions of clinical notes with MonoMiner. Genet Med 2022; 24:2091-2102. [PMID: 35976265 DOI: 10.1016/j.gim.2022.07.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 11/19/2022] Open
Abstract
PURPOSE Cohort building is a powerful foundation for improving clinical care, performing biomedical research, recruiting for clinical trials, and many other applications. We set out to build a cohort of all monogenic patients with a definitive causal gene diagnosis in a 3-million patient hospital system. METHODS We define a subset (4461) of OMIM diseases that have at least 1 known monogenic causal gene. We then introduce MonoMiner, a natural language processing framework to identify molecularly confirmed monogenic patients from free-text clinical notes. RESULTS We show that ICD-10-CM codes cover only a fraction of monogenic diseases and that even where available, ICD-10-CM code‒based patient retrieval offers 0.14 precision. Searching by causal gene symbol offers great recall but has an even worse 0.07 precision. MonoMiner achieves 6 to 11 times higher precision (0.80), with 0.87 precision on disease diagnosis alone, tagging 4259 patients with 560 monogenic diseases and 534 causal genes, at 0.48 recall. CONCLUSION MonoMiner enables the discovery of a large, high-precision cohort of patients with monogenic diseases with an established molecular diagnosis, empowering numerous downstream uses. Because it relies solely on clinical notes, MonoMiner is highly portable, and its approach is adaptable to other domains and languages.
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Affiliation(s)
- David Wei Wu
- Department of Computer Science, Stanford University School of Engineering, Stanford, CA; Medical Scientist Training Program, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | | | - Gill Bejerano
- Department of Computer Science, Stanford University School of Engineering, Stanford, CA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA.
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24
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Benito-Lozano J, Arias-Merino G, Gómez-Martínez M, Ancochea-Díaz A, Aparicio-García A, Posada de la Paz M, Alonso-Ferreira V. Diagnostic Process in Rare Diseases: Determinants Associated with Diagnostic Delay. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19116456. [PMID: 35682039 PMCID: PMC9180264 DOI: 10.3390/ijerph19116456] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 02/01/2023]
Abstract
Many people living with rare disease (RD) report a difficult diagnostic process from the symptom onset until they obtain the definitive diagnosis. The aim of this study was thus to ascertain the diagnostic process in RDs, and explore the determinants related with having to wait for more than one year in this process (defined as “diagnostic delay”). We conducted a case–control study, using a purpose-designed form from the Spanish Rare Diseases Patient Registry for data-collection purposes. A descriptive analysis was performed and multivariate backward logistic regression models fitted. Based on data on 1216 patients living with RDs, we identified a series of determinants associated with experiencing diagnostic delay. These included: having to travel to see a specialist other than that usually consulted in the patient’s home province (OR 2.1; 95%CI 1.6–2.9); visiting more than 10 specialists (OR 2.6; 95%CI 1.7–4.0); being diagnosed in a region other than that of the patient’s residence at the date of symptom onset (OR 2.3; 95%CI 1.5–3.6); suffering from a RD of the nervous system (OR 1.4; 95%CI 1.0–1.8). In terms of time taken to see a specialist, waiting more than 6 months to be referred from the first medical visit was the period of time which most contributed to diagnostic delay (PAR 30.2%). In conclusion, this is the first paper to use a collaborative study based on a nationwide registry to address the diagnostic process of patients living with RDs. While the evidence shows that the diagnostic process experienced by these persons is complex, more studies are needed to determine the implications that this has for their lives and those of their families at a social, educational, occupational, psychological, and financial level.
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Affiliation(s)
- Juan Benito-Lozano
- Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.B.-L.); (G.A.-M.); (M.G.-M.); (M.P.d.l.P.)
- Universidad Nacional de Educación a Distancia (UNED), 28015 Madrid, Spain
| | - Greta Arias-Merino
- Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.B.-L.); (G.A.-M.); (M.G.-M.); (M.P.d.l.P.)
| | - Mario Gómez-Martínez
- Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.B.-L.); (G.A.-M.); (M.G.-M.); (M.P.d.l.P.)
| | | | - Aitor Aparicio-García
- The State Reference Center for Assistance to People Living with Rare Diseases and Their Families (CREER), Centro de Referencia Estatal de Atención a Personas con Enfermedades Raras y sus Familias, Dependiente del IMSERSO, 09001 Burgos, Spain;
| | - Manuel Posada de la Paz
- Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.B.-L.); (G.A.-M.); (M.G.-M.); (M.P.d.l.P.)
| | - Verónica Alonso-Ferreira
- Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.B.-L.); (G.A.-M.); (M.G.-M.); (M.P.d.l.P.)
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 28029 Madrid, Spain
- Correspondence: ; Tel.: +34-91-822-2089
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25
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Chen X, Faviez C, Vincent M, Briseño-Roa L, Faour H, Annereau JP, Lyonnet S, Zaidan M, Saunier S, Garcelon N, Burgun A. Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis. Front Pharmacol 2022; 13:786710. [PMID: 35401179 PMCID: PMC8993144 DOI: 10.3389/fphar.2022.786710] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
A timely diagnosis is a key challenge for many rare diseases. As an expanding group of rare and severe monogenic disorders with a broad spectrum of clinical manifestations, ciliopathies, notably renal ciliopathies, suffer from important underdiagnosis issues. Our objective is to develop an approach for screening large-scale clinical data warehouses and detecting patients with similar clinical manifestations to those from diagnosed ciliopathy patients. We expect that the top-ranked similar patients will benefit from genetic testing for an early diagnosis. The dependence and relatedness between phenotypes were taken into account in our similarity model through medical concept embedding. The relevance of each phenotype to each patient was also considered by adjusted aggregation of phenotype similarity into patient similarity. A ranking model based on the best-subtype-average similarity was proposed to address the phenotypic overlapping and heterogeneity of ciliopathies. Our results showed that using less than one-tenth of learning sources, our language and center specific embedding provided comparable or better performances than other existing medical concept embeddings. Combined with the best-subtype-average ranking model, our patient-patient similarity-based screening approach was demonstrated effective in two large scale unbalanced datasets containing approximately 10,000 and 60,000 controls with kidney manifestations in the clinical data warehouse (about 2 and 0.4% of prevalence, respectively). Our approach will offer the opportunity to identify candidate patients who could go through genetic testing for ciliopathy. Earlier diagnosis, before irreversible end-stage kidney disease, will enable these patients to benefit from appropriate follow-up and novel treatments that could alleviate kidney dysfunction.
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Affiliation(s)
- Xiaoyi Chen
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France.,HeKA, Inria, Paris, France.,Data Science Platform, Imagine Institute, Université de Paris, INSERM UMR 1163, Paris, France
| | - Carole Faviez
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France.,HeKA, Inria, Paris, France
| | - Marc Vincent
- Data Science Platform, Imagine Institute, Université de Paris, INSERM UMR 1163, Paris, France
| | | | - Hassan Faour
- Data Science Platform, Imagine Institute, Université de Paris, INSERM UMR 1163, Paris, France
| | | | | | - Mohamad Zaidan
- Service de Néphrologie, Hôpital Universitaire Bicêtre, Kremlin Bicêtre, France
| | - Sophie Saunier
- Laboratory of Renal Hereditary Diseases, Imagine Institute, Université de Paris, INSERM UMR 1163, Paris, France
| | - Nicolas Garcelon
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France.,HeKA, Inria, Paris, France.,Data Science Platform, Imagine Institute, Université de Paris, INSERM UMR 1163, Paris, France
| | - Anita Burgun
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France.,HeKA, Inria, Paris, France.,Department of Medical Informatics, Hôpital Necker-Enfant Malades, AP-HP, Paris, France
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26
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Bordini BJ. Undiagnosed and Rare Diseases in Critical Care. Crit Care Clin 2022; 38:159-171. [DOI: 10.1016/j.ccc.2021.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Fujiwara T, Shin JM, Yamaguchi A. Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm. Hum Mutat 2022; 43:734-742. [PMID: 35143083 PMCID: PMC9305291 DOI: 10.1002/humu.24341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/17/2022] [Accepted: 02/07/2022] [Indexed: 11/11/2022]
Abstract
Over 10,000 rare genetic diseases have been identified, and millions of newborns are affected by severe rare genetic diseases each year. A variety of Human Phenotype Ontology (HPO)-based clinical decision support systems (CDSS) and patient repositories have been developed to support clinicians in diagnosing patients with suspected rare genetic diseases. In September 2017, we released PubCaseFinder (https://pubcasefinder.dbcls.jp), a web-based CDSS that provides ranked lists of genetic and rare diseases using HPO-based phenotypic similarities, where top-listed diseases represent the most likely differential diagnosis. We also developed a Matchmaker Exchange (MME) application programming interface (API) to query PubCaseFinder, which has been adopted by several patient repositories. In this paper, we describe notable updates regarding PubCaseFinder, the GeneYenta matching algorithm implemented in PubCaseFinder, and the PubCaseFinder API. The updated GeneYenta matching algorithm improves the performance of the CDSS automated differential diagnosis function. Moreover, the updated PubCaseFinder and new API empower patient repositories participating in MME and medical professionals to actively use HPO-based resources. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Toyofumi Fujiwara
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa-shi, Chiba-ken, 277-0871, Japan
| | - Jae-Moon Shin
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa-shi, Chiba-ken, 277-0871, Japan
| | - Atsuko Yamaguchi
- Graduate School of Integrative Science and Engineering, Tokyo City University, Setagaya-ku, Tokyo, 158-8557, Japan
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Álvaro-Sánchez S, Abreu-Rodríguez I, Abulí A, Serra-Juhe C, Garrido-Navas MDC. Current Status of Genetic Counselling for Rare Diseases in Spain. Diagnostics (Basel) 2021; 11:2320. [PMID: 34943558 PMCID: PMC8700506 DOI: 10.3390/diagnostics11122320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/05/2021] [Accepted: 12/06/2021] [Indexed: 12/29/2022] Open
Abstract
Genetic Counselling is essential for providing personalised information and support to patients with Rare Diseases (RD). Unlike most other developed countries, Spain does not recognize geneticists or genetic counsellors as healthcare professionals Thus, patients with RD face not only challenges associated with their own disease but also deal with lack of knowledge, uncertainty, and other psychosocial issues arising as a consequence of diagnostic delay. In this review, we highlight the importance of genetic counsellors in the field of RD as well as evaluate the current situation in which rare disease patients receive genetic services in Spain. We describe the main units and strategies at the national level assisting patients with RD and we conclude with a series of future perspectives and unmet needs that Spain should overcome to improve the management of patients with RD.
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Affiliation(s)
| | - Irene Abreu-Rodríguez
- Genetics Service, Hospital del Mar Research Institute, IMIM, 08003 Barcelona, Spain;
| | - Anna Abulí
- Department of Clinical and Molecular Genetics, Hospital Vall d’Hebron, 08035 Barcelona, Spain;
- Medicine Genetics Group, Vall d’Hebron Research Institute (VHIR), 08035 Barcelona, Spain
| | - Clara Serra-Juhe
- U705 CIBERER, Genetics Department, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain;
- Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), 28029 Madrid, Spain
| | - Maria del Carmen Garrido-Navas
- CONGEN, Genetic Counselling Services, C/Albahaca 4, 18006 Granada, Spain;
- Genetics Department, Faculty of Sciences, Universidad de Granada, 18071 Granada, Spain
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29
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Gilbert S, Gabriel H, Pankow A, Biskup S, Wagner AD. [What is confirmed in the diagnostics of autoinflammatory fever diseases?]. Internist (Berl) 2021; 62:1290-1294. [PMID: 34878559 DOI: 10.1007/s00108-021-01221-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2021] [Indexed: 11/29/2022]
Abstract
Periodic fever syndromes (PFS) are a group of rare autoinflammatory diseases, which are characterized by disorders of the innate immune reaction and life-long recurrent episodes of inflammatory symptoms. This article describes the diagnostic approach. In addition to the patient medical history, physical examination and laboratory determinations, gene tests are becoming increasingly more important. The panel diagnostics using high throughput sequencing or next generation sequencing (NGS) is the method of choice for the detection of a genetic cause of PFS. This article discusses the diagnostic decision support systems (DDSS) that can play a future role in the diagnosis of rare diseases, especially those with complex patterns of symptoms.
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Affiliation(s)
- Stephen Gilbert
- Ada Health GmbH, Karl-Liebknecht-Str. 1, 10178, Berlin, Deutschland.,Else Kröner-Fresenius Center for Digital Health, Faculty of Medicine Carl Gustav Carus, Louisenstr. 120, 61348, Bad Homburg, Deutschland.,Technische Universität Dresden, Dresden, Deutschland
| | - Heinz Gabriel
- Praxis für Humangenetik Tübingen, Paul-Ehrlich-Str. 23, 72076, Tübingen, Deutschland
| | - Anne Pankow
- Abt. für Nieren- und Hochdruckerkrankungen, Ambulanz für seltene entzündliche, Systemerkrankungen mit Nierenbeteiligung, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.,Klinik für Rheumatologie und Immunologie, Berlin, Charité, Charitéplatz 1, 10117, Berlin, Deutschland
| | - Saskia Biskup
- Praxis für Humangenetik Tübingen, Paul-Ehrlich-Str. 23, 72076, Tübingen, Deutschland
| | - Annette Doris Wagner
- Abt. für Nieren- und Hochdruckerkrankungen, Ambulanz für seltene entzündliche, Systemerkrankungen mit Nierenbeteiligung, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.
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30
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Decherchi S, Pedrini E, Mordenti M, Cavalli A, Sangiorgi L. Opportunities and Challenges for Machine Learning in Rare Diseases. Front Med (Lausanne) 2021; 8:747612. [PMID: 34676229 PMCID: PMC8523988 DOI: 10.3389/fmed.2021.747612] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022] Open
Abstract
Rare diseases (RDs) are complicated health conditions that are difficult to be managed at several levels. The scarcity of available data chiefly determines an intricate scenario even for experts and specialized clinicians, which in turn leads to the so called “diagnostic odyssey” for the patient. This situation calls for innovative solutions to support the decision process via quantitative and automated tools. Machine learning brings to the stage a wealth of powerful inference methods; however, matching the health conditions with advanced statistical techniques raises methodological, technological, and even ethical issues. In this contribution, we critically point to the specificities of the dialog of rare diseases with machine learning techniques concentrating on the key steps and challenges that may hamper or create actionable knowledge and value for the patient together with some on-field methodological suggestions and considerations.
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Affiliation(s)
- Sergio Decherchi
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy
| | - Elena Pedrini
- Department of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Marina Mordenti
- Department of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Andrea Cavalli
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Luca Sangiorgi
- Department of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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31
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Dohi E, Bangash AH. Visualizing the phenotype diversity: a case study of Alexander disease. Genomics Inform 2021; 19:e28. [PMID: 34638175 PMCID: PMC8510876 DOI: 10.5808/gi.21016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 08/29/2021] [Indexed: 11/20/2022] Open
Abstract
Since only a small number of patients have a rare disease, it is difficult to identify all of the features of these diseases. This is especially true for patients uncommonly presenting with rare diseases. It can also be difficult for the patient, their families, and even clinicians to know which one of a number of disease phenotypes the patient is exhibiting. To address this issue, during Biomedical Linked Annotation Hackathon 7 (BLAH7), we tried to extract Alexander disease patient data in Portable Document Format. We then visualized the phenotypic diversity of those Alexander disease patients with uncommon presentations. This led to us identifying several issues that we need to overcome in our future work.
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Affiliation(s)
- Eisuke Dohi
- Department of Neuroscience of Disease, Brain Research Institute, Niigata University, Niigata 951-8122, Japan
| | - Ali Haider Bangash
- Shifa College of Medicine, Shifa Tameer-e-Millat University, Islamabad 46000, Pakistan.,COST Action EVidence-Based RESearch (EVBRES), Western Norway University of Applied Sciences, Bergen 5063, Norway
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32
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Hurvitz N, Azmanov H, Kesler A, Ilan Y. Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases. Eur J Hum Genet 2021; 29:1485-1490. [PMID: 34276056 PMCID: PMC8484657 DOI: 10.1038/s41431-021-00928-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/06/2021] [Accepted: 06/16/2021] [Indexed: 02/07/2023] Open
Abstract
Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians. In the present study, we reviewed the relevant literature on the obstacles encountered in the management of patients with rare diseases. Examples of currently available AI platforms are presented. The use of second-generation AI-based systems that are patient-tailored is presented. The system provides a means for early diagnosis and a method for improving the response to therapies based on clinically meaningful outcome parameters. The system may offer a patient-tailored monitoring tool that is based on parameters that are relevant to patients and caregivers and provides a clinically meaningful tool for follow-up. The system can provide an inclusive solution for patients with rare diseases and ensures adherence based on clinical responses. It has the potential advantage of not being dependent on large datasets and is a dynamic system that adapts to ongoing changes in patients' disease and response to therapy.
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Affiliation(s)
- Noa Hurvitz
- Faculty of Medicine, Department of Medicine, Hebrew University, Hadassah Medical Center, Jerusalem, Israel
| | - Henny Azmanov
- Faculty of Medicine, Department of Medicine, Hebrew University, Hadassah Medical Center, Jerusalem, Israel
| | - Asa Kesler
- Faculty of Medicine, Department of Medicine, Hebrew University, Hadassah Medical Center, Jerusalem, Israel
| | - Yaron Ilan
- Faculty of Medicine, Department of Medicine, Hebrew University, Hadassah Medical Center, Jerusalem, Israel.
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33
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Willmen T, Völkel L, Ronicke S, Hirsch MC, Kaufeld J, Rychlik RP, Wagner AD. Health economic benefits through the use of diagnostic support systems and expert knowledge. BMC Health Serv Res 2021; 21:947. [PMID: 34503507 PMCID: PMC8431907 DOI: 10.1186/s12913-021-06926-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 08/20/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Rare diseases are difficult to diagnose. Due to their rarity, heterogeneity, and variability, rare diseases often result not only in extensive diagnostic tests and imaging studies, but also in unnecessary repetitions of examinations, which places a greater overall burden on the healthcare system. Diagnostic decision support systems (DDSS) optimized by rare disease experts and used early by primary care physicians and specialists are able to significantly shorten diagnostic processes. The objective of this study was to evaluate reductions in diagnostic costs incurred in rare disease cases brought about by rapid referral to an expert and diagnostic decision support systems. METHODS Retrospectively, diagnostic costs from disease onset to diagnosis were analyzed in 78 patient cases from the outpatient clinic for rare inflammatory systemic diseases at Hannover Medical School. From the onset of the first symptoms, all diagnostic measures related to the disease were taken from the patient files and documented for each day. The basis for the health economic calculations was the Einheitlicher Bewertungsmaßstab (EBM) used in Germany for statutory health insurance, which assigns a fixed flat rate to the various medical services. For 76 cases we also calculated the cost savings that would have been achieved by the diagnosis support system Ada DX applied by an expert. RESULTS The expert was able to achieve significant savings for patients with long courses of disease. On average, the expert needed only 27 % of the total costs incurred in the individual treatment odysseys to make the correct diagnosis. The expert also needed significantly less time and avoided unnecessary examination repetitions. If a DDSS had been applied early in the 76 cases studied, only 51-68 % of the total costs would have incurred and the diagnosis would have been made earlier. Earlier diagnosis would have significantly reduced costs. CONCLUSION The study showed that significant savings in the diagnostic process of rare diseases can be achieved through rapid referral to an expert and the use of DDSS. Faster diagnosis not only achieves savings, but also enables the right therapy and thus an increase in the quality of life for patients.
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Affiliation(s)
- Tina Willmen
- Department of Nephrology, Hannover Medical School, Hanover, Germany
| | - Lukas Völkel
- Institute for Empirical Health Economics, Burscheid, Germany
| | - Simon Ronicke
- Medical Clinic for Nephrology and Internal Intensive Care Medicine, Charité Berlin, Berlin, Germany
| | - Martin C Hirsch
- Institute for AI in Medicine, University Hospital of Giessen and Marburg, Marburg, Germany
- Ada Health GmbH, Berlin, Germany
| | - Jessica Kaufeld
- Department of Nephrology, Hannover Medical School, Hanover, Germany
| | | | - Annette D Wagner
- Department of Nephrology, Hannover Medical School, Hanover, Germany.
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34
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Zerka F, Urovi V, Bottari F, Leijenaar RTH, Walsh S, Gabrani-Juma H, Gueuning M, Vaidyanathan A, Vos W, Occhipinti M, Woodruff HC, Dumontier M, Lambin P. Privacy preserving distributed learning classifiers - Sequential learning with small sets of data. Comput Biol Med 2021; 136:104716. [PMID: 34364262 DOI: 10.1016/j.compbiomed.2021.104716] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/16/2021] [Accepted: 07/28/2021] [Indexed: 01/09/2023]
Abstract
BACKGROUND Artificial intelligence (AI) typically requires a significant amount of high-quality data to build reliable models, where gathering enough data within a single institution can be particularly challenging. In this study we investigated the impact of using sequential learning to exploit very small, siloed sets of clinical and imaging data to train AI models. Furthermore, we evaluated the capacity of such models to achieve equivalent performance when compared to models trained with the same data over a single centralized database. METHODS We propose a privacy preserving distributed learning framework, learning sequentially from each dataset. The framework is applied to three machine learning algorithms: Logistic Regression, Support Vector Machines (SVM), and Perceptron. The models were evaluated using four open-source datasets (Breast cancer, Indian liver, NSCLC-Radiomics dataset, and Stage III NSCLC). FINDINGS The proposed framework ensured a comparable predictive performance against a centralized learning approach. Pairwise DeLong tests showed no significant difference between the compared pairs for each dataset. INTERPRETATION Distributed learning contributes to preserve medical data privacy. We foresee this technology will increase the number of collaborative opportunities to develop robust AI, becoming the default solution in scenarios where collecting enough data from a single reliable source is logistically impossible. Distributed sequential learning provides privacy persevering means for institutions with small but clinically valuable datasets to collaboratively train predictive AI while preserving the privacy of their patients. Such models perform similarly to models that are built on a larger central dataset.
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Affiliation(s)
- Fadila Zerka
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University, Maastricht, the Netherlands; Radiomics (Oncoradiomics SA), Liège, Belgium.
| | - Visara Urovi
- Institute of Data Science (IDS), Maastricht University, the Netherlands
| | | | | | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | | | - Akshayaa Vaidyanathan
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University, Maastricht, the Netherlands; Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Michel Dumontier
- Institute of Data Science (IDS), Maastricht University, the Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, the Netherlands
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35
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Radin M, Foddai SG, Barinotti A, Cecchi I, Rubini E, Sciascia S, Roccatello D. Reducing the diagnostic delay in Antiphospholipid Syndrome over time: a real world observation. Orphanet J Rare Dis 2021; 16:280. [PMID: 34134750 PMCID: PMC8207757 DOI: 10.1186/s13023-021-01906-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 06/07/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Antiphospholipid Syndrome (APS) is a rare autoimmune disorder with an estimated prevalence of 40-50 cases per 100.000 persons. Patients suffering from low prevalence diseases are more likely to face diagnostic challenges, given the limited knowledge of most clinicians. The main aim of this study was to investigate the time between symptoms occurrence and the diagnosis of APS patients using the Piedmont and Aosta Valley Rare Disease Registry. Secondly, to evaluate the individual impact of the diagnostic gap by gathering patients' personal experiences through a self-administered questionnaire. RESULTS Data from the Piedmont and Aosta Valley Rare Disease Registry was used. In addition, personal experiences were analyzed through a self-administered questionnaire. A total of 740 APS patients included in the Piedmont and Aosta Valley Rare Disease Registry were analyzed. Diagnostic delay (as defined by time between symptoms' occurrence and the diagnosis of APS) was significantly reduced over time. In particular, when comparing the diagnostic delay between patients diagnosed between 1983 and 1999 and patients diagnosed between 2000 and 2015, we found a significant statistical difference (Mann-Whithey U Test; mean rank 1216.6 vs. 1066.9, respectively; p < 0.0001). When analyzing the self-administered questionnaires, patients with a perception of having suffered for a diagnostic delay had a higher prevalence of symptoms suggestive of an autoimmune condition but not highly suggestive of APS (45%), followed by "extra criteria" APS manifestation (30%) and by thrombotic events (25%). The first clinical manifestation of patients who did not have the perception of having suffered a diagnostic delay was thrombotic events (45.5%), followed by autoimmune manifestation not linked to APS (45.5%), and "extra criteria" APS manifestations (9%). CONCLUSIONS While the diagnostic delay of APS has been reduced during the last years, the time between symptoms occurrence and the diagnosis of rare diseases still represents a critical issue to be addressed in order to prevent major complications.
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Affiliation(s)
- Massimo Radin
- Center of Research of Immunopathology and Rare Diseases-Coordinating Center of Piemonte and Valle d'Aosta Network for Rare Diseases, and SCDU Nephrology and Dialysis, Department of Clinical and Biological Sciences, S. Giovanni Bosco Hospital, University of Turin, Piazza del Donatore di Sangue 3, 10154, Turin, Italy
| | - Silvia Grazietta Foddai
- Center of Research of Immunopathology and Rare Diseases-Coordinating Center of Piemonte and Valle d'Aosta Network for Rare Diseases, and SCDU Nephrology and Dialysis, Department of Clinical and Biological Sciences, S. Giovanni Bosco Hospital, University of Turin, Piazza del Donatore di Sangue 3, 10154, Turin, Italy
- School of Specialization of Clinical Pathology, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
| | - Alice Barinotti
- Center of Research of Immunopathology and Rare Diseases-Coordinating Center of Piemonte and Valle d'Aosta Network for Rare Diseases, and SCDU Nephrology and Dialysis, Department of Clinical and Biological Sciences, S. Giovanni Bosco Hospital, University of Turin, Piazza del Donatore di Sangue 3, 10154, Turin, Italy
- School of Specialization of Clinical Pathology, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
| | - Irene Cecchi
- Center of Research of Immunopathology and Rare Diseases-Coordinating Center of Piemonte and Valle d'Aosta Network for Rare Diseases, and SCDU Nephrology and Dialysis, Department of Clinical and Biological Sciences, S. Giovanni Bosco Hospital, University of Turin, Piazza del Donatore di Sangue 3, 10154, Turin, Italy
| | - Elena Rubini
- Center of Research of Immunopathology and Rare Diseases-Coordinating Center of Piemonte and Valle d'Aosta Network for Rare Diseases, and SCDU Nephrology and Dialysis, Department of Clinical and Biological Sciences, S. Giovanni Bosco Hospital, University of Turin, Piazza del Donatore di Sangue 3, 10154, Turin, Italy
| | - Savino Sciascia
- Center of Research of Immunopathology and Rare Diseases-Coordinating Center of Piemonte and Valle d'Aosta Network for Rare Diseases, and SCDU Nephrology and Dialysis, Department of Clinical and Biological Sciences, S. Giovanni Bosco Hospital, University of Turin, Piazza del Donatore di Sangue 3, 10154, Turin, Italy.
- Nephrology and Dialysis, Department of Clinical and Biological Sciences, S. Giovanni Bosco Hospital, University of Turin, Turin, Italy.
| | - Dario Roccatello
- Center of Research of Immunopathology and Rare Diseases-Coordinating Center of Piemonte and Valle d'Aosta Network for Rare Diseases, and SCDU Nephrology and Dialysis, Department of Clinical and Biological Sciences, S. Giovanni Bosco Hospital, University of Turin, Piazza del Donatore di Sangue 3, 10154, Turin, Italy
- Nephrology and Dialysis, Department of Clinical and Biological Sciences, S. Giovanni Bosco Hospital, University of Turin, Turin, Italy
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Ji M, Genchev GZ, Huang H, Xu T, Lu H, Yu G. Evaluation Framework for Successful Artificial Intelligence-Enabled Clinical Decision Support Systems: Mixed Methods Study. J Med Internet Res 2021; 23:e25929. [PMID: 34076581 PMCID: PMC8209524 DOI: 10.2196/25929] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/12/2021] [Accepted: 04/30/2021] [Indexed: 12/13/2022] Open
Abstract
Background Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. Objective The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. Methods An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. Results The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. Conclusions User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.
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Affiliation(s)
- Mengting Ji
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Georgi Z Genchev
- Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.,Bulgarian Institute for Genomics and Precision Medicine, Sofia, Bulgaria
| | - Hengye Huang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ting Xu
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Lu
- Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.,Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Guangjun Yu
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China
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37
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Jiang J, Lei S, Zhu M, Li R, Yue J, Chen J, Li Z, Gong J, Lin D, Wu X, Lin Z, Lin H. Improving the Generalizability of Infantile Cataracts Detection via Deep Learning-Based Lens Partition Strategy and Multicenter Datasets. Front Med (Lausanne) 2021; 8:664023. [PMID: 34026791 PMCID: PMC8137827 DOI: 10.3389/fmed.2021.664023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 03/22/2021] [Indexed: 11/13/2022] Open
Abstract
Infantile cataract is the main cause of infant blindness worldwide. Although previous studies developed artificial intelligence (AI) diagnostic systems for detecting infantile cataracts in a single center, its generalizability is not ideal because of the complicated noises and heterogeneity of multicenter slit-lamp images, which impedes the application of these AI systems in real-world clinics. In this study, we developed two lens partition strategies (LPSs) based on deep learning Faster R-CNN and Hough transform for improving the generalizability of infantile cataracts detection. A total of 1,643 multicenter slit-lamp images collected from five ophthalmic clinics were used to evaluate the performance of LPSs. The generalizability of Faster R-CNN for screening and grading was explored by sequentially adding multicenter images to the training dataset. For the normal and abnormal lenses partition, the Faster R-CNN achieved the average intersection over union of 0.9419 and 0.9107, respectively, and their average precisions are both > 95%. Compared with the Hough transform, the accuracy, specificity, and sensitivity of Faster R-CNN for opacity area grading were improved by 5.31, 8.09, and 3.29%, respectively. Similar improvements were presented on the other grading of opacity density and location. The minimal training sample size required by Faster R-CNN is determined on multicenter slit-lamp images. Furthermore, the Faster R-CNN achieved real-time lens partition with only 0.25 s for a single image, whereas the Hough transform needs 34.46 s. Finally, using Grad-Cam and t-SNE techniques, the most relevant lesion regions were highlighted in heatmaps, and the high-level features were discriminated. This study provides an effective LPS for improving the generalizability of infantile cataracts detection. This system has the potential to be applied to multicenter slit-lamp images.
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Affiliation(s)
- Jiewei Jiang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, China
| | - Shutao Lei
- School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an, China
| | - Mingmin Zhu
- School of Mathematics and Statistics, Xidian University, Xi'an, China
| | - Ruiyang Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jiayun Yue
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, China
| | - Jingjing Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhongwen Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jiamin Gong
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhuoling Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
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38
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Measures of success of computerized clinical decision support systems: An overview of systematic reviews. HEALTH POLICY AND TECHNOLOGY 2021. [DOI: 10.1016/j.hlpt.2020.11.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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39
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Wester L, Mücke M, Bender TTA, Sellin J, Klawonn F, Conrad R, Szczypien N. Pain drawings as a diagnostic tool for the differentiation between two pain-associated rare diseases (Ehlers-Danlos-Syndrome, Guillain-Barré-Syndrome). Orphanet J Rare Dis 2020; 15:323. [PMID: 33203450 PMCID: PMC7672863 DOI: 10.1186/s13023-020-01542-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/11/2020] [Indexed: 11/10/2022] Open
Abstract
Background The diagnosis of rare diseases poses a particular challenge to clinicians. This study analyzes whether patients’ pain drawings (PDs) help in the differentiation of two pain-associated rare diseases, Ehlers-Danlos Syndrome (EDS) and Guillain-Barré Syndrome (GBS). Method The study was designed as a prospective, observational, single-center study. The sample comprised 60 patients with EDS (3 male, 52 female, 5 without gender information; 39.2 ± 11.4 years) and 32 patients with GBS (10 male, 20 female, 2 without gender information; 50.5 ± 13.7 years). Patients marked areas afflicted by pain on a sketch of a human body with anterior, posterior, and lateral views. PDs were electronically scanned and processed. Each PD was classified based on the Ružička similarity to the EDS and the GBS averaged image (pain profile) in a leave-one-out cross validation approach. A receiver operating characteristic (ROC) curve was plotted. Results 60–80% of EDS patients marked the vertebral column with the neck and the tailbone and the knee joints as pain areas, 40–50% the shoulder-region, the elbows and the thumb saddle joint. 60–70% of GBS patients marked the dorsal and plantar side of the feet as pain areas, 40–50% the palmar side of the fingertips, the dorsal side of the left palm and the tailbone. 86% of the EDS patients and 96% of the GBS patients were correctly identified by computing the Ružička similarity. The ROC curve yielded an excellent area under the curve value of 0.95. Conclusion PDs are a useful and economic tool to differentiate between GBS and EDS. Further studies should investigate its usefulness in the diagnosis of other pain-associated rare diseases. This study was registered in the German Clinical Trials Register, No. DRKS00014777 (Deutsches Register klinischer Studien, DRKS), on 01.06.2018.
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Affiliation(s)
- Larissa Wester
- Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany
| | - Martin Mücke
- Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany.
| | | | - Julia Sellin
- Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany
| | - Frank Klawonn
- Institute for Information Engineering, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany.,Biostatistics Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Rupert Conrad
- Department of Psychosomatic Medicine and Psychotherapy, University Hospital Bonn, Bonn, Germany
| | - Natasza Szczypien
- Institute for Information Engineering, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
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40
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Schon KR, Ratnaike T, van den Ameele J, Horvath R, Chinnery PF. Mitochondrial Diseases: A Diagnostic Revolution. Trends Genet 2020; 36:702-717. [PMID: 32674947 DOI: 10.1016/j.tig.2020.06.009] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 06/18/2020] [Accepted: 06/19/2020] [Indexed: 12/26/2022]
Abstract
Mitochondrial disorders have emerged as a common cause of inherited disease, but are traditionally viewed as being difficult to diagnose clinically, and even more difficult to comprehensively characterize at the molecular level. However, new sequencing approaches, particularly whole-genome sequencing (WGS), have dramatically changed the landscape. The combined analysis of nuclear and mitochondrial DNA (mtDNA) allows rapid diagnosis for the vast majority of patients, but new challenges have emerged. We review recent discoveries that will benefit patients and families, and highlight emerging questions that remain to be resolved.
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Affiliation(s)
- Katherine R Schon
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK; Medical Research Council (MRC) Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Thiloka Ratnaike
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK; Medical Research Council (MRC) Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK; Department of Paediatrics, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Jelle van den Ameele
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK; Medical Research Council (MRC) Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Rita Horvath
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Patrick F Chinnery
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK; Medical Research Council (MRC) Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.
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