The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. Here, we present MINIMAR (MINimum Information for Medical AI Reporting), a proposal describing the minimum information necessary to understand intended predictions, target populations, and hidden biases, and the ability to generalize these emerging technologies. We call for a standard to accurately and responsibly report on AI in health care. This will facilitate the design and implementation of these models and promote the development and use of associated clinical decision support tools, as well as manage concerns regarding accuracy and bias.

ÖzAmaç: Sensorinöral işitme kaybı, otoimmün ve inflamatuvar hastalıkların bir komplikasyonu olarak ortaya çıkabilmektedir. Psoriasis toplumda yaygın olarak görülmesine rağmen, odyolojik bozukluklar ile ilişkisi hakkında literatürdeki bilgiler sınırlıdır ve vestibüler bozukluklarla ilişkisi daha önce araştırılmamıştır. Biz de bu çalışmamızda, psoriasis hastalarında odyovestibüler bozuklukların varlığını ve hastalık parametreleri ile ilişkisini araştırmayı amaçladık. Yöntemler: Altmış bir psoriasis hastası ile yaş ve cinsiyet uyumlu 61 sağlıklı gönüllü çalışmaya dahil edildi. İşitme ve denge bozukluğuna yol açabilecek olası etiyolojik faktörlere sahip olanlar çalışmaya dahil edilmedi. Tüm katılımcılara öncelikle tam bir kulak, burun ve boğaz muayenesi yapıldı. Takiben hastalara ses izolasyonu sağlanmış odyoloji laboratuvarında tam odyolojik tetkik (saf ses odyometri, otoakustik emisyon, stapes refleksi, konuşmayı alma ve ayırt etme eşiği saptanması) ve elektronistagmografi testleri yapıldı. Psoriasis hastalık şiddeti psoriasis alan ve şiddet indeksi, vücut yüzey alanı ve araştırmacının genel değerlendirmesi ile değerlendirildi. Bulgular: Odyometrik testlerde hasta ve kontroller arasında anlamlı farklılık saptandı.Objective: Sensorineural hearing loss can occur as a complication of autoimmune and inflammatory diseases. Although psoriasis is also a chronic inflammatory skin disease characterized by T-cell mediated hyper proliferation of the keratinocytes, the information about the relationship between audiological disorders is limited in the literature and the relationship with vestibular disorders has not been investigated before. In this study, we aimed to investigate the presence of audiovestibular disorders and their relationship with disease parameters. Methods: Sixty-one patients with psoriasis and 61 healthy individuals were included in this prospective cross-sectional study. Those with possible etiologic factors that may lead to hearing and balance disorders were not included in the study. All participants were first performed a full ear, nose and throat examination. Subsequently, full audiological examination (pure audiometry, autoacoustic emission, stapes reflex, detection threshold of speech and discrimination) and electronystagmography tests were performed in the audiology laboratory where sound isolation was provided. Psoriasis severity was assessed by psoriasis area and severity index, body surface area and general evaluation of researcher. Results: There were significant differences between patients and controls in terms of audiovestibular symptoms. According to audiograms, predominant bilateral sensorineural hearing loss was detected in high frequency in psoriasis patients. The vestibular abnormalities in patients with psoriasis were found to be more frequent than those in controls, only saccadic test values were observed as statistically significant. Conclusion: Our study demonstrates that audiovestibular abnormalities are significantly associated with psoriasis. Therefore, patients with psoriasis s...

NLR was correlated with important prognostic markers in PAH such as NYHA FC, BNP and TAPSE. This simple marker may be useful in the assessment of disease severity in patients with PAH.

No abstract

Objective To evaluate a system we developed that connects natural language processing (NLP) for information extraction from narrative text mammography reports with a Bayesian network for decision-support about breast cancer diagnosis. The ultimate goal of this system is to provide decision support as part of the workflow of producing the radiology report. Materials and methods We built a system that uses an NLP information extraction system (which extract BI-RADS descriptors and clinical information from mammography reports) to provide the necessary inputs to a Bayesian network (BN) decision support system (DSS) that estimates lesion malignancy from BI-RADS descriptors. We used this integrated system to predict diagnosis of breast cancer from radiology text reports and evaluated it with a reference standard of 300 mammography reports. We collected two different outputs from the DSS: (1) the probability of malignancy and (2) the BI-RADS final assessment category. Since NLP may produce imperfect inputs to the DSS, we compared the difference between using perfect (“reference standard”) structured inputs to the DSS (“RS-DSS”) vs NLP-derived inputs (“NLP-DSS”) on the output of the DSS using the concordance correlation coefficient. We measured the classification accuracy of the BI-RADS final assessment category when using NLP-DSS, compared with the ground truth category established by the radiologist. Results The NLP-DSS and RS-DSS had closely matched probabilities, with a mean paired difference of 0.004 ± 0.025. The concordance correlation of these paired measures was 0.95. The accuracy of the NLP-DSS to predict the correct BI-RADS final assessment category was 97.58%. Conclusion The accuracy of the information extracted from mammography reports using the NLP system was sufficient to provide accurate DSS results. We believe our system could ultimately reduce the variation in practice in mammography related to assessment of malignant lesions and improve management decisions.