Histopathologic diagnosis of Hirschsprung's disease (HSCR) is time consuming and requires expertise. The use of artificial intelligence (AI) in digital pathology is actively researched and may improve the diagnosis of HSCR. The purpose of this research was to develop an algorithm capable of identifying ganglion cells in digital pathology slides and implement it as an assisting tool for the pathologist in the diagnosis of HSCR. Ninety five digital pathology slides were used for the construction and training of the algorithm. Fifty cases suspected for HSCR (727 slides) were used as a validation cohort. Image sets suspected to contain ganglion cells were chosen by the algorithm and then reviewed and scored by five pathologists, one HSCR expert and 4 non-experts. The algorithm was able to identify ganglion cells with 96% sensitivity and 99% specificity (in normal colon) as well as to correctly identify a case previously misdiagnosed as non-HSCR. The expert was able to achieve perfectly accurate diagnoses based solely on the images suggested by the algorithm, with over 95% time saved. Non-experts would require expert consultation in 20–58% of the cases to achieve similar results. The use of AI in the diagnosis of HSCR can greatly reduce the time and effort required for diagnosis and improve accuracy.
Objective This study aimed to identify the frequency of potentially preventable causes of stillbirth in a large heterogeneous population. Study Design This is a retrospective study of all stillbirth cases between January 2011 and December 2016 at a single tertiary medical center. Deliveries resulting from a nonviable fetus prior to 24 weeks of gestation, intrapartum fetal death, and incomplete stillbirth workup were excluded. Potentially preventable stillbirth was defined as that of a nonanomalous fetus that most likely resulted from one or more of the following: (1) placental-mediated complications, (2) postterm pregnancy, (3) monochorionicity-associated complications, (4) cholestasis of pregnancy, (5) preventable or treatable infections, and (6) isoimmunization. Results During the study period, 312 stillbirths were identified, 228 of which met the inclusion criteria. Of the 110 cases with a recognized cause, 47 (20.6%) were potentially preventable. The most common causes were placental-mediated complications and preventable or treatable infections, accounting for 75 and 9% of all potentially preventable causes, respectively. There were no recognizable maternal risk factors for potentially preventable stillbirth. Conclusion One-fifth of all causes of stillbirth are potentially preventable. Due to the significant contribution of placental-mediated complications to preventable stillbirth, close sonographic surveillance and timely delivery may decrease risk substantially.
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